NZ754048B2 - Systems and methods to exploit areas of coherence in wireless systems - Google Patents

Abstract

Disclosed is a method for establishing a multi-user multi-antenna system (MU-MAS). The method includes the steps of distributing a plurality of transceiver devices and connected antennas of the MU-MAS and simultaneously transmitting pre-coded radio signals from the plurality of distributed transceiver devices. The pre-coding causes deliberate radio frequency interference to create concurrent, non-interfering enclosed shapes in space of coherent wireless signals. The pre-coding is adjusted to adapt a size of the enclosed shapes in space. The size of the enclosed shapes in space is adapted by any one of selecting a subset of distributed transceiver devices, adjusting spacing of the distributed transceiver devices, adjusting elevations of the distributed transceiver devices, and adjusting angular diversity of the distributed transceiver devices. er devices. The pre-coding causes deliberate radio frequency interference to create concurrent, non-interfering enclosed shapes in space of coherent wireless signals. The pre-coding is adjusted to adapt a size of the enclosed shapes in space. The size of the enclosed shapes in space is adapted by any one of selecting a subset of distributed transceiver devices, adjusting spacing of the distributed transceiver devices, adjusting elevations of the distributed transceiver devices, and adjusting angular diversity of the distributed transceiver devices.

Description

Our Ref: BST200Patents Form No. 5PATENTS ACT 1953Divisional Application out of:New Zealand Patent Application No 738000 which is a divisional out ofNew Zealand Patent Application No. 717370 which is a divisional out ofNew Zealand Patent Application No. 622137 which entered the National Phase in NewZealand on 7 March 2014 from dated 12 September 2012 andclaiming priority from US Patent Application Nos. 13/232,996 and 13/233,006 filed14 September 2011C C C CO O O OM M M MP P P PL L L LE E E ET T T TE E E E S S S SP P P PE E E EC C C CIIIIF F F FIIIIC C C CA A A AT T T TIIIIO O O ON N N NSYSTEMS AND METHODS TO EXPLOIT AREAS OFCOHERENCE IN WIRELESS SYSTEMSWe, Rearden, LLC, of 355 Bryant Street, Suite 110, San Francisco, California 94107,United States of America, do hereby declare the invention for which we pray that apatent may be granted to us, and the method by which it is to be performed, to beparticularly described in and by the following statement:SYSTEMS AND METHODS TO EXPLOIT AREAS OF COHERENCE IN WIRELESSSYSTEMSRELATED APPLICATIONS This application is a continuation-in-part of the following co-pending U.S.

Patent Applications: U.S. Application Serial No. 12/917,257, filed November 1, 2010, entitled“Systems And Methods To Coordinate Transmissions In Distributed WirelessSystems Via User Clustering” U.S. Application Serial No. 12/802,988, filed June 16, 2010, entitled“Interference Management, Handoff, Power Control And Link Adaptation InDistributed-Input Distributed-Output (DIDO) Communication Systems” U.S. Application Serial No. 12/802,976, filed June 16, 2010, entitled“System And Method For Adjusting DIDO Interference Cancellation Based On SignalStrength Measurements” U.S. Application Serial No. 12/802,974, filed June 16, 2010, entitled“System And Method For Managing Inter-Cluster Handoff Of Clients Which TraverseMultiple DIDO Clusters” U.S. Application Serial No. 12/802,989, filed June 16, 2010, entitled“System And Method For Managing Handoff Of A Client Between DifferentDistributed-Input-Distributed-Output (DIDO) Networks Based On Detected VelocityOf The Client” U.S. Application Serial No. 12/802,958, filed June 16, 2010, entitled“System And Method For Power Control And Antenna Grouping In A Distributed-Input-Distributed-Output (DIDO) Network” U.S. Application Serial No. 12/802,975, filed June 16, 2010, entitled“System And Method For Link adaptation In DIDO Multicarrier Systems” U.S. Application Serial No. 12/802,938, filed June 16, 2010, entitled“System And Method For DIDO Precoding Interpolation In Multicarrier Systems” U.S. Application Serial No. 12/630,627, filed December 3, 2009, entitled”System and Method For Distributed Antenna Wireless Communications” U.S. Application Serial No. 12/143,503, filed June 20, 2008 entitled”System and Method For Distributed Input-Distributed Output WirelessCommunications”; U.S. Application Serial No. 11/894,394, filed August 20, 2007 entitled,”System and Method for Distributed Input Distributed Output WirelessCommunications”; U.S. Application Serial No. 11/894,362, filed August 20, 2007 entitled,“System and method for Distributed Input-Distributed Wireless Communications”; U.S. Application Serial No. 11/894,540, filed August 20, 2007 entitled“System and Method For Distributed Input-Distributed Output WirelessCommunications” U.S. Application Serial No. 11/256,478, filed October 21, 2005 entitled“System and Method For Spatial-Multiplexed Tropospheric ScatterCommunications”; U.S. Application Serial No. 10/817,731, filed April 2, 2004 entitled“System and Method For Enhancing Near Vertical Incidence Skywave (“NVIS”)Communication Using Space-Time Coding.

BACKGROUND Prior art multi-user wireless systems may include only a single basestation or several base stations.

A single WiFi base station (e.g., utilizing 2.4 GHz 802.11b, g or nprotocols) attached to a broadband wired Internet connection in an area where thereare no other WiFi access points (e.g. a WiFi access point attached to DSL within arural home) is an example of a relatively simple multi-user wireless system that is asingle base station that is shared by one or more users that are within itstransmission range. If a user is in the same room as the wireless access point, theuser will typically experience a high-speed link with few transmission disruptions(e.g. there may be packet loss from 2.4GHz interferers, like microwave ovens, butnot from spectrum sharing with other WiFi devices), If a user is a medium distanceaway or with a few obstructions in the path between the user and WiFi access point,the user will likely experience a medium-speed link. If a user is approaching the edgeof the range of the WiFi access point, the user will likely experience a low-speed link,and may be subject to periodic drop-outs if changes to the channel result in thesignal SNR dropping below usable levels. And, finally, if the user is beyond the rangeof the WiFi base station, the user will have no link at all.

When multiple users access the WiFi base station simultaneously, thenthe available data throughput is shared among them. Different users will typicallyplace different throughput demands on a WiFi base station at a given time, but attimes when the aggregate throughput demands exceed the available throughputfrom the WiFi base station to the users, then some or all users will receive less datathroughput than they are seeking. In an extreme situation where a WiFi access pointis shared among a very large number of users, throughput to each user can slowdown to a crawl, and worse, data throughput to each user may arrive in short burstsseparated by long periods of no data throughput at all, during which time other usersare served. This “choppy” data delivery may impair certain applications, like mediastreaming.

Adding additional WiFi base stations in situations with a large number ofusers will only help up to a point. Within the 2.4GHz ISM band in the U.S., there are3 non-interfering channels that can be used for WiFi, and if 3 WiFi base stations inthe same coverage area are configured to each use a different non-interferingchannel, then the aggregate throughput of the coverage area among multiple userswill be increased up to a factor of 3. But, beyond that, adding more WiFi basestations in the same coverage area will not increase aggregate throughput, sincethey will start sharing the same available spectrum among them, effectually utilizingtime-division multiplexed access (TDMA) by “taking turns” using the spectrum. Thissituation is often seen in coverage areas with high population density, such as withinmulti-dwelling units. For example, a user in a large apartment building with a WiFiadapter may well experience very poor throughput due to dozens of other interferingWiFi networks (e.g. in other apartments) serving other users that are in the samecoverage area, even if the user’s access point is in the same room as the clientdevice accessing the base station. Although the link quality is likely good in thatsituation, the user would be receiving interference from neighbor WiFi adaptersoperating in the same frequency band, reducing the effective throughput to the user.

Current multiuser wireless systems, including both unlicensed spectrum,such as WiFi, and licensed spectrum, suffer from several limitations. These includecoverage area, downlink (DL) data rate and uplink (UL) data rate. Key goals of nextgeneration wireless systems, such as WiMAX and LTE, are to improve coveragearea and DL and UL data rate via multiple-input multiple-output (MIMO) technology.

MIMO employs multiple antennas at transmit and receive sides of wireless links toimprove link quality (resulting in wider coverage) or data rate (by creating multiplenon-interfering spatial channels to every user). If enough data rate is available forevery user (note, the terms “user” and “client” are used herein interchangeably),however, it may be desirable to exploit channel spatial diversity to create non-interfering channels to multiple users (rather than single user), according to multiuserMIMO (MU-MIMO) techniques. See, e.g., the following references: G. Caire and S. Shamai, “On the achievable throughput of amultiantenna Gaussian broadcast channel,” IEEE Trans. Info.Th., vol. 49, pp. 1691–1706, July 2003.

P. Viswanath and D. Tse, “Sum capacity of the vector Gaussianbroadcast channel and uplink-downlink duality,” IEEE Trans. Info. Th., vol. 49, pp.1912–1921, Aug. 2003.

S. Vishwanath, N. Jindal, and A. Goldsmith, “Duality, achievable rates,and sum-rate capacity of Gaussian MIMO broadcast channels,” IEEE Trans. Info.

Th., vol. 49, pp. 2658–2668, Oct. 2003.

W. Yu and J. Cioffi, “Sum capacity of Gaussian vector broadcastchannels,” IEEE Trans. Info. Th., vol. 50, pp. 1875–1892, Sep. 2004.

M. Costa, “Writing on dirty paper,” IEEE Transactions on InformationTheory, vol. 29, pp. 439–441, May 1983.

M. Bengtsson, “A pragmatic approach to multi-user spatial multiplexing,”Proc. of Sensor Array and Multichannel Sign.Proc. Workshop, pp. 130–134, Aug.2002.

K.-K. Wong, R. D. Murch, and K. B. Letaief, “Performance enhancementof multiuser MIMO wireless communication systems,” IEEE Trans. Comm., vol. 50,pp. 1960–1970, Dec. 2002.

M. Sharif and B. Hassibi, “On the capacity of MIMO broadcast channelwith partial side information,” IEEE Trans. Info.Th., vol. 51, pp. 506–522, Feb. 2005.

For example, in MIMO 4x4 systems (i.e., four transmit and four receiveantennas), 10MHz bandwidth, 16-QAM modulation and forward error correction(FEC) coding with rate 3/4 (yielding spectral efficiency of 3bps/Hz), the ideal peakdata rate achievable at the physical layer for every user is 4x30Mbps=120Mbps,which is much higher than required to deliver high definition video content (whichmay only require ~10Mbps). In MU-MIMO systems with four transmit antennas, fourusers and single antenna per user, in ideal scenarios (i.e., independent identicallydistributed, i.i.d., channels) downlink data rate may be shared across the four usersand channel spatial diversity may be exploited to create four parallel 30Mbps datalinks to the users.

Different MU-MIMO schemes have been proposed as part of the LTE standard asdescribed, for example, in 3GPP, “Multiple Input Multiple Output in UTRA”, 3GPP TR.876 V7.0.0, Mar. 2007; 3GPP, “Base Physical channels and modulation”, TS36.211, V8.7.0, May 2009; and 3GPP, “Multiplexing and channel coding”, TS 36.212,V8.7.0, May 2009. However, these schemes can provide only up to 2X improvementin DL data rate with four transmit antennas. Practical implementations of MU-MIMOtechniques in standard and proprietary cellular systems by companies likeArrayComm (see, e.g., ArrayComm, “Field-proven results”,http://www.arraycomm.com/serve.php?page=proof) have yielded up to a ~3Xincrease (with four transmit antennas) in DL data rate via space division multipleaccess (SDMA). A key limitation of MU-MIMO schemes in cellular networks is lackof spatial diversity at the transmit side. Spatial diversity is a function of antennaspacing and multipath angular spread in the wireless links. In cellular systemsemploying MU-MIMO techniques, transmit antennas at a base station are typicallyclustered together and placed only one or two wavelengths apart due to limited realestate on antenna support structures (referred to herein as “towers,” whetherphysically tall or not) and due to limitations on where towers may be located.

Moreover, multipath angular spread is low since cell towers are typically placed highup (10 meters or more) above obstacles to yield wider coverage.

Other practical issues with cellular system deployment include excessivecost and limited availability of locations for cellular antenna locations (e.g. due tomunicipal restrictions on antenna placement, cost of real-estate, physicalobstructions, etc.) and the cost and/or availability of network connectivity to thetransmitters (referred to herein as “backhaul”). Further, cellular systems often havedifficulty reaching clients located deeply in buildings due to losses from walls,ceilings, floors, furniture and other impediments.

Indeed, the entire concept of a cellular structure for wide-area networkwireless presupposes a rather rigid placement of cellular towers, an alternation offrequencies between adjacent cells, and frequently sectorization, so as to avoidinterference among transmitters (either base stations or users) that are using thesame frequency. As a result, a given sector of a given cell ends up being a sharedblock of DL and UL spectrum among all of the users in the cell sector, which is thenshared among these users primarily in only the time domain. For example, cellularsystems based on Time Division Multiple Access (TDMA) and Code Division MultipleAccess (CDMA) both share spectrum among users in the time domain. Byoverlaying such cellular systems with sectorization, perhaps a 2-3X spatial domainbenefit can be achieved. And, then by overlaying such cellular systems with a MU-MIMO system, such as those described previously, perhaps another 2-3X space-time domain benefit can be achieved. But, given that the cells and sectors of thecellular system are typically in fixed locations, often dictated by where towers can beplaced, even such limited benefits are difficult to exploit if user density (or data ratedemands) at a given time does not match up well with tower/sector placement. Acellular smart phone user often experiences the consequence of this today wherethe user may be talking on the phone or downloading a web page without anytrouble at all, and then after driving (or even walking) to a new location will suddenlysee the voice quality drop or the web page slow to a crawl, or even lose theconnection entirely. But, on a different day, the user may have the exact oppositeoccur in each location. What the user is probably experiencing, assuming theenvironmental conditions are the same, is the fact that user density (or data ratedemands) is highly variable, but the available total spectrum (and thereby total datarate, using prior art techniques) to be shared among users at a given location islargely fixed.

Further, prior art cellular systems rely upon using different frequencies indifferent adjacent cells, typically 3 different frequencies. For a given amount ofspectrum, this reduces the available data rate by 3X.

So, in summary, prior art cellular systems may lose perhaps 3X inspectrum utilization due to cellularization, and may improve spectrum utilization byperhaps 3X through sectorization and perhaps 3X more through MU-MIMOtechniques, resulting in a net 3*3/3 = 3X potential spectrum utilization. Then, thatbandwidth is typically divided up among users in the time domain, based upon whatsector of what cell the users fall into at a given time. There are even furtherinefficiencies that result due to the fact that a given user’s data rate demands aretypically independent of the user’s location, but the available data rate variesdepending on the link quality between the user and the base station. For example, auser further from a cellular base station will typically have less available data ratethan a user closer to a base station. Since the data rate is typically shared among allof the users in a given cellular sector, the result of this is that all users are impactedby high data rate demands from distant users with poor link quality (e.g. on the edgeof a cell) since such users will still demand the same amount of data rate, yet theywill be consuming more of the shared spectrum to get it.

Other proposed spectrum sharing systems, such as that used by WiFi(e.g., 802.11b, g, and n) and those proposed by the White Spaces Coalition, sharespectrum very inefficiently since simultaneous transmissions by base stations withinrange of a user result in interference, and as such, the systems utilize collisionavoidance and sharing protocols. These spectrum sharing protocols are within thetime domain, and so, when there are a large number of interfering base stations andusers, no matter how efficient each base station itself is in spectrum utilization,collectively the base stations are limited to time domain sharing of the spectrumamong each other. Other prior art spectrum sharing systems similarly rely uponsimilar methods to mitigate interference among base stations (be they cellular basestations with antennas on towers or small scale base stations, such as WiFi AccessPoints (APs)). These methods include limiting transmission power from the basestation so as to limit the range of interference, beamforming (via synthetic or physicalmeans) to narrow the area of interference, time-domain multiplexing of spectrumand/or MU-MIMO techniques with multiple clustered antennas on the user device,the base station or both. And, in the case of advanced cellular networks in place orplanned today, frequently many of these techniques are used at once.

But, what is apparent by the fact that even advanced cellular systems canachieve only about a 3X increase in spectrum utilization compared to a single userutilizing the spectrum is that all of these techniques have done little to increase theaggregate data rate among shared users for a given area of coverage. In particular,as a given coverage area scales in terms of users, it becomes increasingly difficult toscale the available data rate within a given amount of spectrum to keep pace withthe growth of users. For example, with cellular systems, to increase the aggregatedata rate within a given area, typically the cells are subdivided into smaller cells(often called nano-cells or femto-cells). Such small cells can become extremelyexpensive given the limitations on where towers can be placed, and the requirementthat towers must be placed in a fairly structured pattern so as to provide coveragewith a minimum of “dead zones”, yet avoid interference between nearby cells usingthe same frequencies. Essentially, the coverage area must be mapped out, theavailable locations for placing towers or base stations must be identified, and thengiven these constraints, the designers of the cellular system must make do with thebest they can. And, of course, if user data rate demands grow over time, then thedesigners of the cellular system must yet again remap the coverage area, try to findlocations for towers or base stations, and once again work within the constraints ofthe circumstances. And, very often, there simply is no good solution, resulting indead zones or inadequate aggregate data rate capacity in a coverage area. In otherwords, the rigid physical placement requirements of a cellular system to avoidinterference among towers or base stations utilizing the same frequency results insignificant difficulties and constraints in cellular system design, and often is unable tomeet user data rate and coverage requirements.

So-called prior art “cooperative” and “cognitive” radio systems seek toincrease the spectral utilization in a given area by using intelligent algorithms withinradios such that they can minimize interference among each other and/or such thatthey can potentially “listen” for other spectrum use so as to wait until the channel isclear. Such systems are proposed for use particularly in unlicensed spectrum in aneffort to increase the spectrum utilization of such spectrum.

A mobile ad hoc network (MANET) (see http://en.wikipedia.org/wiki/Mobile_ad_hoc_network) is an example of a cooperative self-configuring networkintended to provide peer-to-peer communications, and could be used to establishcommunication among radios without cellular infrastructure, and with sufficiently low-power communications, can potentially mitigate interference among simultaneoustransmissions that are out of range of each other. A vast number of routing protocolshave been proposed and implemented for MANET systems (seehttp://en.wikipedia.org/wiki/List_of_ad-hoc_routing_protocols for a list of dozens ofrouting protocols in a wide range of classes), but a common theme among them isthey are all techniques for routing (e.g. repeating) transmissions in such a way tominimize transmitter interference within the available spectrum, towards the goal ofparticular efficiency or reliability paradigms.

All of the prior art multi-user wireless systems seek to improve spectrumutilization within a given coverage area by utilizing techniques to allow forsimultaneous spectrum utilization among base stations and multiple users. Notably,in all of these cases, the techniques utilized for simultaneous spectrum utilizationamong base stations and multiple users achieve the simultaneous spectrum use bymultiple users by mitigating interference among the waveforms to the multiple users.

For example, in the case of 3 base stations each using a different frequency totransmit to one of 3 users, there interference is mitigated because the 3transmissions are at 3 different frequencies. In the case of sectorization from a basestation to 3 different users, each 180 degrees apart relative to the base station,interference is mitigated because the beamforming prevents the 3 transmissionsfrom overlapping at any user.

When such techniques are augmented with MU-MIMO, and, for example,each base station has 4 antennas, then this has the potential to increase downlinkthroughput by a factor of 4, by creating four non-interfering spatial channels to theusers in given coverage area. But it is still the case that some technique must beutilized to mitigate the interference among multiple simultaneous transmissions tomultiple users in different coverage areas.

And, as previously discussed, such prior art techniques (e.g.cellularization, sectorization) not only typically suffer from increasing the cost of themulti-user wireless system and/or the flexibility of deployment, but they typically runinto physical or practical limitations of aggregate throughput in a given coveragearea. For example, in a cellular system, there may not be enough available locationsto install more base stations to create smaller cells. And, in an MU-MIMO system,given the clustered antenna spacing at each base station location, the limited spatialdiversity results in asymptotically diminishing returns in throughput as more antennasare added to the base station.

And further, in the case of multi-user wireless systems where the userlocation and density is unpredictable, it results in unpredictable (with frequentlyabrupt changes) in throughput, which is inconvenient to the user and renders someapplications (e.g. the delivery of services requiring predictable throughput)impractical or of low quality. Thus, prior art multi-user wireless systems still leavemuch to be desired in terms of their ability to provide predictable and/or high-qualityservices to users.

Despite the extraordinary sophistication and complexity that has beendeveloped for prior art multi-user wireless systems over time, there exist commonthemes: transmissions are distributed among different base stations (or ad hoctransceivers) and are structured and/or controlled so as to avoid the RF waveformtransmissions from the different base stations and/or different ad hoc transceiversfrom interfering with each other at the receiver of a given user.

Or, to put it another way, it is taken as a given that if a user happens toreceive transmissions from more than one base station or ad hoc transceiver at thesame time, the interference from the multiple simultaneous transmissions will resultin a reduction of the SNR and/or bandwidth of the signal to the user which, if severeenough, will result in loss of all or some of the potential data (or analog information)that would otherwise have been received by the user.

Thus, in a multiuser wireless system, it is necessary to utilize one or morespectrum sharing approaches or another to avoid or mitigate such interference tousers from multiple base stations or ad hoc transceivers transmitting at the samefrequency at the same time. There are a vast number of prior art approaches toavoiding such interference, including controlling base stations’ physical locations(e.g. cellularization), limiting power output of base stations and/or ad hoctransceivers (e.g. limiting transmit range), beamforming/sectorization, and timedomain multiplexing. In short, all of these spectrum sharing systems seek to addressthe limitation of multiuser wireless systems that when multiple base stations and/orad hoc transceivers transmitting simultaneously at the same frequency are receivedby the same user, the resulting interference reduces or destroys the data throughputto the affected user. If a large percentage, or all, of the users in the multi-userwireless system are subject to interference from multiple base stations and/or ad hoctransceivers (e.g. in the event of the malfunction of a component of a multi-userwireless system), then it can result in a situation where the aggregate throughput ofthe multi-user wireless system is dramatically reduced, or even rendered non-functional..

Prior art multi-user wireless systems add complexity and introducelimitations to wireless networks and frequently result in a situation where a givenuser’s experience (e.g. available bandwidth, latency, predictability, reliability) isimpacted by the utilization of the spectrum by other users in the area. Given theincreasing demands for aggregate bandwidth within wireless spectrum shared bymultiple users, and the increasing growth of applications that can rely upon multi-user wireless network reliability, predictability and low latency for a given user, it isapparent that prior art multi-user wireless technology suffers from many limitations.

Indeed, with the limited availability of spectrum suitable for particular types ofwireless communications (e.g. at wavelengths that are efficient in penetratingbuilding walls), it may be the case that prior art wireless techniques will beinsufficient to meet the increasing demands for bandwidth that is reliable, predictableand low-latency.

Prior art related to the current invention describes beamforming systemsand methods for null-steering in multiuser scenarios. Beamforming was originallyconceived to maximize received signal-to-noise ratio (SNR) by dynamically adjustingphase and/or amplitude of the signals (i.e., beamforming weights) fed to theantennas of the array, thereby focusing energy toward the user’s direction. Inmultiuser scanarios, beamforming can be used to suppress interfering sources andmaximize signal-to-interference-plus-noise ratio (SINR). For example, whenbeamforming is used at the receiver of a wireless link, the weights are computed tocreate nulls in the direction of the interfering sources. When beamforming is used atthe transmitter in multiuser downlink scenarios, the weights are calculated to pre-cancel inter-user interfence and maximize the SINR to every user. Alternativetechniques for multiuser systems, such as BD precoding, compute the precodingweights to maximize throughput in the downlink broadcast channel. The co-pendingapplications, which are incorporated herein by reference, describe the foregoingtechniques (see co-pending applications for specific citations).

Reference to any prior art in this specification does not constitute anadmission that such prior art forms part of the common general knowledge.

It is an object of the present invention to provide an antenna system andmethod overcoming at least some of the problems of the prior art or to at leastprovide the public with a useful choice.

According to a first aspect there is provided a method comprising:distributing a plurality of transceiver devices and connected antennas of amulti-user multi-antenna system (MU-MAS);simultaneously transmitting pre-coded radio signals from the plurality ofdistributed transceiver devices, the pre-coding causing deliberate radio frequencyinterference to create concurrent, non-interfering enclosed shapes in space ofcoherent wireless signals,adjusting the pre-coding to adapt a size of the enclosed shapes in space, andadapting the size of the enclosed shapes in space by selecting a subset ofdistributed transceiver devices.

According to a further aspect there is provided a method comprising:distributing a plurality of transceiver devices and connected antennas of amulti-user multi-antenna system (MU-MAS);simultaneously transmitting pre-coded radio signals from the plurality ofdistributed transceiver devices, the pre-coding causing deliberate radio frequencyinterference to create concurrent, non-interfering enclosed shapes in space ofcoherent wireless signals,adjusting the pre-coding to adapt a size of the enclosed shapes in space, andchanging the size of the enclosed shapes in space by adjusting spacing of thedistributed transceiver devices.

According to a still further aspect there is provided a method comprising:distributing a plurality of transceiver devices and connected antennas of amulti-user multi-antenna system (MU-MAS);simultaneously transmitting pre-coded radio signals from the plurality ofdistributed transceiver devices, the pre-coding causing deliberate radio frequencyinterference to create concurrent, non-interfering enclosed shapes in space ofcoherent wireless signals,adjusting the pre-coding to adapt a size of the enclosed shapes in space, andchanging the size of the enclosed shapes in space by adjusting elevations ofthe distributed transceiver devices.

According to a yet further aspect there is provided a method comprising:distributing a plurality of transceiver devices and connected antennas of amulti-user multi-antenna system (MU-MAS);simultaneously transmitting pre-coded radio signals from the plurality ofdistributed transceiver devices, the pre-coding causing deliberate radio frequencyinterference to create concurrent, non-interfering enclosed shapes in space ofcoherent wireless signals,adjusting the pre-coding to adapt a size of the enclosed shapes in space, andchanging the size of the enclosed shapes in space by adjusting angulardiversity of the distributed transceiver devices.

It is acknowledged that the terms "comprise", "comprises" and "comprising"may, under varying jurisdictions, be attributed with either an exclusive or an inclusivemeaning. For the purpose of this specification, and unless otherwise noted, theseterms are intended to have an inclusive meaning - i.e. they will be taken to mean aninclusion of the listed components that the use directly references, but optionally alsothe inclusion of other non-specified components or elements.

BRIEF DESCRIPTION OF THE DRAWINGS A better understanding of the present invention can be obtained from thefollowing detailed description in conjunction with the drawings, in which: illustrates a main DIDO cluster surrounded by neighboring DIDOclusters in one embodiment of the invention. illustrates frequency division multiple access (FDMA) techniquesemployed in one embodiment of the invention. illustrates time division multiple access (TDMA) techniquesemployed in one embodiment of the invention. illustrates different types of interfering zones addressed in oneembodiment of the invention. illustrates a framework employed in one embodiment of theinvention. illustrates a graph showing SER as a function of the SNR,assuming SIR=10dB for the target client in the interfering zone. illustrates a graph showing SER derived from two IDCI-precodingtechniques. illustrates an exemplary scenario in which a target client movesfrom a main DIDO cluster to an interfering cluster. illustrates the signal-to-interference-plus-noise ratio (SINR) as afunction of distance (D). illustrates the symbol error rate (SER) performance of the threescenarios for 4-QAM modulation in flat-fading narrowband channels. illustrates a method for IDCI precoding according to oneembodiment of the invention. illustrates the SINR variation in one embodiment as a function ofthe client’s distance from the center of main DIDO clusters. illustrates one embodiment in which the SER is derived for 4-QAM modulation. illustrates one embodiment of the invention in which a finite statemachine implements a handoff algorithm. illustrates depicts one embodiment of a handoff strategy in thepresence of shadowing. illustrates a hysteresis loop mechanism when switching betweenany two states in Fig. 93. illustrates one embodiment of a DIDO system with power control. illustrates the SER versus SNR assuming four DIDO transmitantennas and four clients in different scenarios. illustrates MPE power density as a function of distance from thesource of RF radiation for different values of transmit power according to oneembodiment of the invention.

FIGS. 20a-b illustrate different distributions of low-power and high-powerDIDO distributed antennas.

FIGS. 21a-b illustrate two power distributions corresponding to theconfigurations in Figs. 20a and 20b, respectively. a-b illustrate the rate distribution for the two scenarios shown inFigs. 99a and 99b, respectively. illustrates one embodiment of a DIDO system with power control. illustrates one embodiment of a method which iterates across allantenna groups according to Round-Robin scheduling policy for transmitting data. illustrates a comparison of the uncoded SER performance ofpower control with antenna grouping against conventional eigenmode selection inU.S. Patent No. 7,636,381.

FIGS. 26a-c illustrate three scenarios in which BD precoding dynamicallyadjusts the precoding weights to account for different power levels over the wirelesslinks between DIDO antennas and clients. illustrates the amplitude of low frequency selective channels(assuming = 1 ) over delay domain or instantaneous PDP (upper plot) andfrequency domain (lower plot) for DIDO 2x2 systems illustrates one embodiment of a channel matrix frequencyresponse for DIDO 2x2, with a single antenna per client. illustrates one embodiment of a channel matrix frequencyresponse for DIDO 2x2, with a single antenna per client for channels characterizedby high frequency selectivity (e.g., with = 0.1 ). illustrates exemplary SER for different QAM schemes (i.e., 4-QAM, 16-QAM, 64-QAM). illustrates one embodiment of a method for implementing linkadaptation (LA) techniques. illustrates SER performance of one embodiment of the linkadaptation (LA) techniques. illustrates the entries of the matrix in equation (28) as a functionof the OFDM tone index for DIDO 2x2 systems with = 64 and = 8. illustrates the SER versus SNR for = 8, M=N =2 transmitantennas and a variable number of P. illustrates the SER performance of one embodiment of aninterpolation method for different DIDO orders and = 16. illustrates one embodiment of a system which employs super-clusters, DIDO-clusters and user-clusters. illustrates a system with user clusters according to oneembodiment of the invention.

FIGS. 38a-b illustrate link quality metric thresholds employed in oneembodiment of the invention.

FIGS. 39-41 illustrate examples of link-quality matrices for establishinguser clusters. illustrates an embodiment in which a client moves across differentdifferent DIDO clusters.

FIGS. 43-46 illustrate relationships between the resolution of sphericalarrays and their area A in one embodiment of the invention. illustrates the degrees of freedom of an exemplary MIMO systemin practical indoor and outdoor propagation scenarios. illustrates the degrees of freedom in an exemplary DIDO systemas a function of the array diameter. illustrates a plurality of centralized processors and distributednodes. illustrates a configuration with both unlicensed nodes andlicensed nodes. illustrates an embodiment where obsolete unlicensed nodes arecovered with a cross. illustrates one embodiment of a cloud wireless system wheredifferent nodes communicate with different centralized processors.

DETAILED DESCRIPTION One solution to overcome many of the above prior art limitations is anembodiment of Distributed-Input Distributed-Output (DIDO) technology. DIDOtechnology is described in the following patents and patent applications, all of whichare assigned the assignee of the present patent and are incorporated by reference.

These patents and applications are sometimes referred to collectively herein as the“related patents and applications”: U.S. Application Serial No. 12/917,257, filed November 1, 2010, entitled“Systems And Methods To Coordinate Transmissions In Distributed WirelessSystems Via User Clustering” U.S. Application Serial No. 12/802,988, filed June 16, 2010, entitled“Interference Management, Handoff, Power Control And Link Adaptation InDistributed-Input Distributed-Output (DIDO) Communication Systems” U.S. Application Serial No. 12/802,976, filed June 16, 2010, entitled“System And Method For Adjusting DIDO Interference Cancellation Based On SignalStrength Measurements” U.S. Application Serial No. 12/802,974, filed June 16, 2010, entitled“System And Method For Managing Inter-Cluster Handoff Of Clients Which TraverseMultiple DIDO Clusters” U.S. Application Serial No. 12/802,989, filed June 16, 2010, entitled“System And Method For Managing Handoff Of A Client Between DifferentDistributed-Input-Distributed-Output (DIDO) Networks Based On Detected VelocityOf The Client” U.S. Application Serial No. 12/802,958, filed June 16, 2010, entitled“System And Method For Power Control And Antenna Grouping In A Distributed-Input-Distributed-Output (DIDO) Network” U.S. Application Serial No. 12/802,975, filed June 16, 2010, entitled“System And Method For Link adaptation In DIDO Multicarrier Systems” U.S. Application Serial No. 12/802,938, filed June 16, 2010, entitled“System And Method For DIDO Precoding Interpolation In Multicarrier Systems” U.S. Application Serial No. 12/630,627, filed December 2, 2009, entitled”System and Method For Distributed Antenna Wireless Communications” U.S. Patent No. 7,599,420, filed August 20, 2007, issued Oct. 6, 2009,entitled “System and Method for Distributed Input Distributed Output WirelessCommunication”; U.S. Patent No. 7,633,994, filed August 20, 2007, issued Dec. 15, 2009,entitled “System and Method for Distributed Input Distributed Output WirelessCommunication”; U.S. Patent No. 7,636,381, filed August 20, 2007, issued Dec. 22, 2009,entitled “System and Method for Distributed Input Distributed Output WirelessCommunication”; U.S. Application Serial No. 12/143,503, filed June 20, 2008 entitled,”System and Method For Distributed Input-Distributed Output WirelessCommunications”; U.S. Application Serial No. 11/256,478, filed October 21, 2005 entitled“System and Method For Spatial-Multiplexed Tropospheric ScatterCommunications”; U.S. Patent No. 7,418,053, filed July 30, 2004, issued August 26, 2008,entitled “System and Method for Distributed Input Distributed Output WirelessCommunication”; U.S. Application Serial No. 10/817,731, filed April 2, 2004 entitled“System and Method For Enhancing Near Vertical Incidence Skywave (“NVIS”)Communication Using Space-Time Coding.

To reduce the size and complexity of the present patent application, thedisclosure of some of the related patents and applications is not explicitly set forthbelow. Please see the related patents and applications for a full detailed descriptionof the disclosure.

Note that section I below (Disclosure From Related Application Serial No.12/802,988) utilizes its own set of endnotes which refer to prior art references andprior applications assigned to the assignee of the present application. The endnotecitations are listed at the end of section I (just prior to the heading for Section II).

Citations in Section II uses may have numerical designations for its citations whichoverlap with those used in Section I even through these numerical designationsidentify different references (listed at the end of Section II). Thus, referencesidentified by a particular numerical designation may be identified within the section inwhich the numerical designation is used.

I. Disclosure From Related Application Serial No. 12/802,9881. Methods to Remove Inter-cluster Interference Described below are wireless radio frequency (RF) communicationsystems and methods employing a plurality of distributed transmitting antennas tocreate locations in space with zero RF energy. When M transmit antennas areemployed, it is possible to create up to (M-1) points of zero RF energy in predefinedlocations. In one embodiment of the invention, the points of zero RF energy arewireless devices and the transmit antennas are aware of the channel stateinformation (CSI) between the transmitters and the receivers. In one embodiment,the CSI is computed at the receivers and fed back to the transmitters. In anotherembodiment, the CSI is computed at the transmitter via training from the receivers,assuming channel reciprocity is exploited. The transmitters may utilize the CSI todetermine the interfering signals to be simultaneously transmitted. In oneembodiment, block diagonalization (BD) precoding is employed at the transmitantennas to generate points of zero RF energy.

The system and methods described herein differ from the conventionalreceive/transmit beamforming techniques described above. In fact, receivebeamforming computes the weights to suppress interference at the receive side (vianull-steering), whereas some embodiments of the invention described herein applyweights at the transmit side to create interference patters that result in one ormultiple locations in space with “zero RF energy.” Unlike conventional transmitbeamforming or BD precoding designed to maximize signal quality (or SINR) toevery user or downlink throughput, respectively, the systems and methods describedherein minimize signal quality under certain conditions and/or from certaintransmitters, thereby creating points of zero RF energy at the client devices(sometimes referred to herein as “users”). Moreover, in the context of distributed-input distributed-output (DIDO) systems (described in our related patents andapplications), transmit antennas distributed in space provide higher degrees offreedom (i.e., higher channel spatial diversity) that can be exploited to create multiplepoints of zero RF energy and/or maximum SINR to different users. For example, withM transmit antennas it is possible to create up to (M-1) points of RF energy. Bycontrast, practical beamforming or BD multiuser systems are typically designed withclosely spaced antennas at the transmit side that limit the number of simultaneoususers that can be serviced over the wireless link, for any number of transmitantennas M.

Consider a system with M transmit antennas and K users, with K<M. Weassume the transmitter is aware of the CSI ( ∈ ∁ ) between the M transmitantennas and K users. For simplicity, every user is assumed to be equipped withsingle antenna, but the same method can be extended to multiple receive antennasper user. The precoding weights ( ∈ ∁ ) that create zero RF energy at the Kusers’ locations are computed to satisfy the following condition = where is the vector with all zero entries and H is the channel matrix obtainedby combining the channel vectors ( ∈ ∁ ) from the M transmit antennas to theK users as = .

In one embodiment, singular value decomposition (SVD) of the channel matrix His computed and the precoding weight w is defined as the right singular vectorcorresponding to the null subspace (identified by zero singular value) of H.

The transmit antennas employ the weight vector defined above to transmit RFenergy, while creating K points of zero RF energy at the locations of the K userssuch that the signal received at the k user is given byr = s + n = 0 + n where n ∈ ∁ is the additive white Gaussian noise (AWGN) at the k user.

In one embodiment, singular value decomposition (SVD) of the channel matrix H iscomputed and the precoding weight w is defined as the right singular vectorcorresponding to the null subspace (identified by zero singular value) of H.

In another embodiment, the wireless system is a DIDO system andpoints of zero RF energy are created to pre-cancel interference to the clientsbetween different DIDO coverage areas. In U.S. Application Serial No. 12/630,627, aDIDO system is described which includes:• DIDO clients• DIDO distributed antennas• DIDO base transceiver stations (BTS)• DIDO base station network (BSN)Every BTS is connected via the BSN to multiple distributed antennas that provideservice to given coverage area called DIDO cluster. In the present patent applicationwe describe a system and method for removing interference between adjacent DIDOclusters. As illustrated in Figure 1, we assume the main DIDO cluster hosts the client(i.e. a user device served by the multi-user DIDO system) affected by interference (ortarget client) from the neighbor clusters.

In one embodiment, neighboring clusters operate at differentfrequencies according to frequency division multiple access (FDMA) techniquessimilar to conventional cellular systems. For example, with frequency reuse factor of3, the same carrier frequency is reused every third DIDO cluster as illustrated inFigure 2. In Figure 2, the different carrier frequencies are identified as F1, F2 and F3.

While this embodiment may be used in some implementations, this solution yieldsloss in spectral efficiency since the available spectrum is divided in multiplesubbands and only a subset of DIDO clusters operate in the same subband.

Moreover, it requires complex cell planning to associate different DIDO clusters todifferent frequencies, thereby preventing interference. Like prior art cellular systems,such cellular planning requires specific placement of antennas and limiting oftransmit power to as to avoid interference between clusters using the samefrequency.

In another embodiment, neighbor clusters operate in the samefrequency band, but at different time slots according to time division multiple access(TDMA) technique. For example, as illustrated in Figure 3 DIDO transmission isallowed only in time slots T , T , and T for certain clusters, as illustrated. Time slots1 2 3can be assigned equally to different clusters, such that different clusters arescheduled according to a Round-Robin policy. If different clusters are characterizedby different data rate requirements (i.e., clusters in crowded urban environments asopposed to clusters in rural areas with fewer number of clients per area of coverage),different priorities are assigned to different clusters such that more time slots areassigned to the clusters with larger data rate requirements. While TDMA asdescribed above may be employed in one embodiment of the invention, a TDMAapproach may require time synchronization across different clusters and may resultin lower spectral efficiency since interfering clusters cannot use the same frequencyat the same time.

In one embodiment, all neighboring clusters transmit at the same timein the same frequency band and use spatial processing across clusters to avoidinterference. In this embodiment, the multi-cluster DIDO system: (i) usesconventional DIDO precoding within the main cluster to transmit simultaneous non-interfering data streams within the same frequency band to multiple clients (such asdescribed in the related patents and applications, including 7,599,420; 7,633,994;7,636,381; and Application Serial No. 12/143,503); (ii) uses DIDO precoding withinterference cancellation in the neighbor clusters to avoid interference to the clientslying in the interfering zones 8010 in Figure 4, by creating points of zero radiofrequency (RF) energy at the locations of the target clients. If a target client is in aninterfering zone 410, it will receive the sum of the RF containing the data streamfrom the main cluster 411 and the zero RF energy from the interfering cluster 412-413, which will simply be the RF containing the data stream from the main cluster.

Thus, adjacent clusters can utilize the same frequency simultaneously without targetclients in the interfering zone suffering from interference.

In practical systems, the performance of DIDO precoding may beaffected by different factors such as: channel estimation error or Doppler effects(yielding obsolete channel state information at the DIDO distributed antennas);intermodulation distortion (IMD) in multicarrier DIDO systems; time or frequencyoffsets. As a result of these effects, it may be impractical to achieve points of zeroRF energy. However, as long as the RF energy at the target client from theinterfering clusters is negligible compared to the RF energy from the main cluster,the link performance at the target client is unaffected by the interference. Forexample, let us assume the client requires 20dB signal-to-noise ratio (SNR) todemodulate 4-QAM constellations using forward error correction (FEC) coding toachieve target bit error rate (BER) of 10 . If the RF energy at the target clientreceived from the interfering cluster is 20dB below the RF energy received from themain cluster, the interference is negligible and the client can demodulate datasuccessfully within the predefined BER target. Thus, the term “zero RF energy” asused herein does not necessarily mean that the RF energy from interfering RFsignals is zero. Rather, it means that the RF energy is sufficiently low relative to theRF energy of the desired RF signal such that the desired RF signal may be receivedat the receiver. Moreover, while certain desirable thresholds for interfering RF energyrelative to desired RF energy are described, the underlying principles of the inventionare not limited to any particular threshold values.

There are different types of interfering zones 8010 as shown in Figure4. For example, “type A” zones (as indicated by the letter “A” in Figure 80) areaffected by interference from only one neighbor cluster, whereas “type B” zones (asindicated by the letter “B”) account for interference from two or multiple neighborclusters.

Figure 5 depicts a framework employed in one embodiment of theinvention. The dots denote DIDO distributed antennas, the crosses refer to theDIDO clients and the arrows indicate the directions of propagation of RF energy. TheDIDO antennas in the main cluster transmit precoded data signals to the clients MC501 in that cluster. Likewise, the DIDO antennas in the interfering cluster serve theclients IC 502 within that cluster via conventional DIDO precoding. The green cross503 denotes the target client TC 503 in the interfering zone. The DIDO antennas inthe main cluster 511 transmit precoded data signals to the target client (blackarrows) via conventional DIDO precoding. The DIDO antennas in the interferingcluster 512 use precoding to create zero RF energy towards the directions of thetarget client 503 (green arrows).

The received signal at target client k in any interfering zone 410A, B inFigure 4 is given by∑ ∑ ∑ = + + + (1) , , , where k=1,…,K, with K being the number of clients in the interfering zone 8010A, B,U is the number of clients in the main DIDO cluster, C is the number of interferingDIDO clusters 412-413 and is the number of clients in the interfering cluster c.

Moreover, ∈ ∁ is the vector containing the receive data streams at client k,assuming M transmit DIDO antennas and N receive antennas at the client devices; ∈ ∁ is the vector of transmit data streams to client k in the main DIDO cluster; ∈ ∁ is the vector of transmit data streams to client u in the main DIDO cluster; ∈ ∁ is the vector of transmit data streams to client i in the c interfering DIDOcluster; ∈ ∁ is the vector of additive white Gaussian noise (AWGN) at the Nreceive antennas of client k; ∈ ∁ is the DIDO channel matrix from the M transmitDIDO antennas to the N receive antennas at client k in the main DIDO cluster; ∈∁ is the DIDO channel matrix from the M transmit DIDO antennas to the N receiveth antennas t client k in the c interfering DIDO cluster; ∈ ∁ is the matrix of DIDOprecoding weights to client k in the main DIDO cluster; ∈ ∁ is the matrix of DIDOprecoding weights to client u in the main DIDO cluster; ∈ ∁ is the matrix ofDIDO precoding weights to client i in the c interfering DIDO cluster.

To simplify the notation and without loss of generality, we assume allclients are equipped with N receive antennas and there are M DIDO distributedantennas in every DIDO cluster, with ≥ ( ∙ ) and ≥ ( ∙ ), ∀ = 1, … , . If Mis larger than the total number of receive antennas in the cluster, the extra transmitantennas are used to pre-cancel interference to the target clients in the interferingzone or to improve link robustness to the clients within the same cluster via diversityschemes described in the related patents and applications, including 7,599,420;7,633,994; 7,636,381; and Application Serial No. 12/143,503.

The DIDO precoding weights are computed to pre-cancel inter-clientinterference within the same DIDO cluster. For example, block diagonalization (BD)precoding described in the related patents and applications, including 7,599,420;7,633,994; 7,636,381; and Application Serial No. 12/143,503 and [7] can be used toremove inter-client interference, such that the following condition is satisfied in themain cluster = ; ∀ = 1, … , ; with ≠ . (2)The precoding weight matrices in the neighbor DIDO clusters are designedsuch thatthe following condition is satisfied = ; ∀ = 1, … , and ∀ = 1, … , . (3), , To compute the precoding matrices , the downlink channel from the M transmitantennas to the clients in the interfering cluster as well as to client k in the interferingzone is estimated and the precoding matrix is computed by the DIDO BTS in theinterfering cluster. If BD method is used to compute the precoding matrices in theinterfering clusters, the following effective channel matrix is built to compute theweights to the i client in the neighbor clusters = (4)(∙ )where is the matrix obtained from the channel matrix ∈ ∁ for the, interfering cluster c, where the rows corresponding to the i client are removed.

Substituting conditions (2) and (3) into (1), we obtain the received data streams fortarget client k, where intra-cluster and inter-cluster interference is removed = + . (5) The precoding weights in (1) computed in the neighbor clusters are designed totransmit precoded data streams to all clients in those clusters, while pre-cancellinginterference to the target client in the interfering zone. The target client receivesprecoded data only from its main cluster. In a different embodiment, the same datastream is sent to the target client from both main and neighbor clusters to obtaindiversity gain. In this case, the signal model in (5) is expressed as = + + (6) , , where is the DIDO precoding matrix from the DIDO transmitters in the c clusterto the target client k in the interfering zone. Note that the method in (6) requires timesynchronization across neighboring clusters, which may be complex to achieve inlarge systems, but nonetheless, is quite feasible if the diversity gain benefit justifiesthe cost of implementation.

We begin by evaluating the performance of the proposed method interms of symbol error rate (SER) as a function of the signal-to-noise ratio (SNR).

Without loss of generality, we define the following signal model assuming singleantenna per client and reformulate (1) as = √SNR + √INR + (7) , , , where INR is the interference-to-noise ratio defined as INR=SNR/SIR and SIR is thesignal-to-interference ratio.

Figure 6 shows the SER as a function of the SNR, assumingSIR=10dB for the target client in the interfering zone. Without loss of generality, wemeasured the SER for 4-QAM and 16-QAM without forwards error correction (FEC)coding. We fix the target SER to 1% for uncoded systems. This target correspondsto different values of SNR depending on the modulation order (i.e., SNR=20dB for 4-QAM and SNR=28dB for 16-QAM). Lower SER targets can be satisfied for the samevalues of SNR when using FEC coding due to coding gain. We consider the scenarioof two clusters (one main cluster and one interfering cluster) with two DIDO antennasand two clients (equipped with single antenna each) per cluster. One of the clients inthe main cluster lies in the interfering zone. We assume flat-fading narrowbandchannels, but the following results can be extended to frequency selectivemulticarrier (OFDM) systems, where each subcarrier undergoes flat-fading. Weconsider two scenarios: (i) one with inter-DIDO-cluster interference (IDCI) where theprecoding weights are computed without accounting for the target client in theinterfering zone; and (ii) the other where the IDCI is removed by computing theweights to cancel IDCI to the target client. We observe that in presence of IDCIthe SER is high and above the predefined target. With IDCI-precoding at theneighbor cluster the interference to the target client is removed and the SER targetsare reached for SNR>20dB.

The results in Figure 6 assumes IDCI-precoding as in (5). If IDCI-precoding at the neighbor clusters is also used to precode data streams to the targetclient in the interfering zone as in (6), additional diversity gain is obtained. Figure 7compares the SER derived from two techniques: (i) “Method 1” using the IDCI-precoding in (5); (ii) “Method 2” employing IDCI-precoding in (6) where the neighborclusters also transmit precoded data stream to the target client. Method 2 yields~3dB gain compared to conventional IDCI-precoding due to additional array gainprovided by the DIDO antennas in the neighbor cluster used to transmit precodeddata stream to the target client. More generally, the array gain of Method 2 overMethod 1 is proportional to 10*log10(C+1), where C is the number of neighborclusters and the factor “1” refers to the main cluster.

Next, we evaluate the performance of the above method as a functionof the target client’s location with respect to the interfering zone. We consider onesimple scenario where a target client 8401 moves from the main DIDO cluster 802 tothe interfering cluster 803, as depicted in Figure 8. We assume all DIDO antennas812 within the main cluster 802 employ BD precoding to cancel intra-clusterinterference to satisfy condition (2). We assume single interfering DIDO cluster,single receiver antenna at the client device 801 and equal pathloss from all DIDOantennas in the main or interfering cluster to the client (i.e., DIDO antennas placed incircle around the client). We use one simplified pathloss model with pathlossexponent 4 (as in typical urban environments) [11].

The analysis hereafter is based on the following simplified signal model that extends(7) to account for pathloss∙ ∙ = + + (8) , , , where the signal-to-interference (SIR) is derived as SIR=((1-D)/D) . In modeling theIDCI, we consider three scenarios: i) ideal case with no IDCI; ii) IDCI pre-cancelled viaBD precoding in the interfering cluster to satisfy condition (3); iii) with IDCI, not pre-cancelled by the neighbor cluster.

Figure 9 shows the signal-to-interference-plus-noise ratio (SINR) as afunction of D (i.e., as the target client moves from the main cluster 802 towards theDIDO antennas 813 in the interfering cluster 8403). The SINR is derived as the ratioof signal power and interference plus noise power using the signal model in (8). Weassume that D =0.1 and SNR=50dB for D=D . In absence of IDCI the wireless linkperformance is only affected by noise and the SINR decreases due to pathloss. Inpresence of IDCI (i.e., without IDCI-precoding) the interference from the DIDOantennas in the neighbor cluster contributes to reduce the SINR.

Figure 10 shows the symbol error rate (SER) performance of the threescenarios above for 4-QAM modulation in flat-fading narrowband channels. TheseSER results correspond to the SINR in Figure 9. We assume SER threshold of 1%for uncoded systems (i.e., without FEC) corresponding to SINR thresholdSINR =20dB in Figure 9. The SINR threshold depends on the modulation orderused for data transmission. Higher modulation orders are typically characterized byhigher SINRT to achieve the same target error rate. With FEC, lower target SER canbe achieved for the same SINR value due to coding gain. In case of IDCI withoutprecoding, the target SER is achieved only within the range D<0.25. With IDCI-precoding at the neighbor cluster the range that satisfies the target SER is extendedup to D<0.6. Beyond that range, the SINR increases due to pathloss and the SERtarget is not satisfied.

One embodiment of a method for IDCI precoding is shown in Figure 11and consists of the following steps:• SIR estimate 1101: Clients estimate the signal power from the main DIDOcluster (i.e., based on received precoded data) and the interference-plus-noise signalpower from the neighbor DIDO clusters. In single-carrier DIDO systems, the framestructure can be designed with short periods of silence. For example, periods ofsilence can be defined between training for channel estimation and precoded datatransmissions during channel state information (CSI) feedback. In one embodiment,the interference-plus-noise signal power from neighbor clusters is measured duringthe periods of silence from the DIDO antennas in the main cluster. In practical DIDOmulticarrier (OFDM) systems, null tones are typically used to prevent direct current(DC) offset and attenuation at the edge of the band due to filtering at transmit andreceive sides. In another embodiment employing multicarrier systems, theinterference-plus-noise signal power is estimated from the null tones. Correctionfactors can be used to compensate for transmit/receive filter attenuation at the edgeof the band. Once the signal-plus-interference-and-noise power (PS) from the maincluster and the interference-plus-noise power from neighbor clusters (P ) areestimated, the client computes the SINR as SINR = . (9)Alternatively, the SINR estimate is derived from the received signal strength indication(RSSI) used in typical wireless communication systems to measure the radio signalpower.

We observe the metric in (9) cannot discriminate between noise and interferencepower level. For example, clients affected by shadowing (i.e., behind obstacles thatattenuate the signal power from all DIDO distributed antennas in the main cluster) ininterference-free environments may estimate low SINR even though they are notaffected by inter-cluster interference.

A more reliable metric for the proposed method is the SIR computed as SIR = (10) where P is the noise power. In practical multicarrier OFDM systems, the noise powerP in (10) is estimated from the null tones, assuming all DIDO antennas from mainand neighbor clusters use the same set of null tones. The interference-plus-noisepower (P ), is estimated from the period of silence as mentioned above. Finally, thesignal-plus-interference-and-noise power (PS) is derived from the data tones. Fromthese estimates, the client computes the SIR in (10).

• Channel estimation at neighbor clusters 1102-1103: If the estimatedSIR in (10) is below predefined threshold (SIRT), determined at 8702 in Figure 11, theclient starts listening to training signals from neighbor clusters. Note that SIR dependson the modulation and FEC coding scheme (MCS) used for data transmission.

Different SIR targets are defined depending on the client’s MCS. When DIDOdistributed antennas from different clusters are time-synchronized (i.e., locked to thesame pulse-per-second, PPS, time reference), the client exploits the trainingsequence to deliver its channel estimates to the DIDO antennas in the neighborclusters at 8703. The training sequence for channel estimation in the neighbor clustersare designed to be orthogonal to the training from the main cluster. Alternatively, whenDIDO antennas in different clusters are not time-synchronized, orthogonal sequences(with good cross-correlation properties) are used for time synchronization in differentDIDO clusters. Once the client locks to the time/frequency reference of the neighborclusters, channel estimation is carried out at 1103.

• IDCI Precoding 1104: Once the channel estimates are available at theDIDO BTS in the neighbor clusters, IDCI-precoding is computed to satisfy thecondition in (3). The DIDO antennas in the neighbor clusters transmit precoded datastreams only to the clients in their cluster, while pre-cancelling interference to theclients in the interfering zone 410 in Figure 4. We observe that if the client lies in thetype B interfering zone 410 in Figure 4, interference to the client is generated bymultiple clusters and IDCI-precoding is carried out by all neighbor clusters at the sametime.

Methods for Handoff Hereafter, we describe different handoff methods for clients that moveacross DIDO clusters populated by distributed antennas that are located in separateareas or that provide different kinds of services (i.e., low- or high-mobility services).a. Handoff Between Adjacent DIDO Clusters In one embodiment, the IDCI-precoder to remove inter-clusterinterference described above is used as a baseline for handoff methods in DIDOsystems. Conventional handoff in cellular systems is conceived for clients to switchseamlessly across cells served by different base stations. In DIDO systems, handoffallows clients to move from one cluster to another without loss of connection.

To illustrate one embodiment of a handoff strategy for DIDO systems,we consider again the example in Figure 8 with only two clusters 802 and 803. Asthe client 801 moves from the main cluster (C1) 802 to the neighbor cluster (C2) 803,one embodiment of a handoff method dynamically calculates the signal quality indifferent clusters and selects the cluster that yields the lowest error rate performanceto the client.

Figure 12 shows the SINR variation as a function of the client’sdistance from the center of clusters C1. For 4-QAM modulation without FEC coding,we consider target SINR=20dB. The line identified by circles represents the SINR forthe target client being served by the DIDO antennas in C1, when both C1 and C2use DIDO precoding without interference cancellation. The SINR decreases as afunction of D due to pathloss and interference from the neighboring cluster. WhenIDCI-precoding is implemented at the neighboring cluster, the SINR loss is only dueto pathloss (as shown by the line with triangles), since interference is completelyremoved. Symmetric behavior is experienced when the client is served from theneighboring cluster. One embodiment of the handoff strategy is defined such that, asthe client moves from C1 to C2, the algorithm switches between different DIDOschemes to maintain the SINR above predefined target.

From the plots in Figure 12, we derive the SER for 4-QAM modulationin Figure 13. We observe that, by switching between different precoding strategies,the SER is maintained within predefined target.

One embodiment of the handoff strategy is as follows.

• C1-DIDO and C2-DIDO precoding: When the client lies within C1, awayfrom the interfering zone, both clusters C1 and C2 operate with conventional DIDOprecoding independently.

• C1-DIDO and C2-IDCI precoding: As the client moves towards theinterfering zone, its SIR or SINR degrades. When the target SINR is reached, thetarget client starts estimating the channel from all DIDO antennas in C2 and providesthe CSI to the BTS of C2. The BTS in C2 computes IDCI-precoding and transmits toall clients in C2 while preventing interference to the target client. For as long as thetarget client is within the interfering zone, it will continue to provide its CSI to both C1and C2.

• C1-IDCI and C2-DIDO precoding: As the client moves towards C2, its SIRor SINR keeps decreasing until it again reaches a target. At this point the client decidesto switch to the neighbor cluster. In this case, C1 starts using the CSI from the targetclient to create zero interference towards its direction with IDCI-precoding, whereasthe neighbor cluster uses the CSI for conventional DIDO-precoding. In oneembodiment, as the SIR estimate approaches the target, the clusters C1 and C2 tryboth DIDO- and IDCI-precoding schemes alternatively, to allow the client to estimatethe SIR in both cases. Then the client selects the best scheme, to maximize certainerror rate performance metric. When this method is applied, the cross-over point forthe handoff strategy occurs at the intersection of the curves with triangles and rhombusin Figure 12. One embodiment uses the modified IDCI-precoding method describedin (6) where the neighbor cluster also transmits precoded data stream to the targetclient to provide array gain. With this approach the handoff strategy is simplified, sincethe client does not need to estimate the SINR for both strategies at the cross-overpoint.

• C1-DIDO and C2-DIDO precoding: As the client moves out of theinterference zone towards C2, the main cluster C1 stops pre-cancelling interferencetowards that target client via IDCI-precoding and switches back to conventional DIDO-precoding to all clients remaining in C1. This final cross-over point in our handoffstrategy is useful to avoid unnecessary CSI feedback from the target client to C1,thereby reducing the overhead over the feedback channel. In one embodiment asecond target SINRT2 is defined. When the SINR (or SIR) increases above this target,the strategy is switched to C1-DIDO and C2-DIDO. In one embodiment, the cluster C1keeps alternating between DIDO- and IDCI-precoding to allow the client to estimatethe SINR. Then the client selects the method for C1 thatmore closely approaches the target SINRT1 from above.

The method described above computes the SINR or SIR estimates fordifferent schemes in real time and uses them to select the optimal scheme. In oneembodiment, the handoff algorithm is designed based on the finite-state machineillustrated in Figure 14. The client keeps track of its current state and switches to thenext state when the SINR or SIR drops below or above the predefined thresholdsillustrated in Figure 12. As discussed above, in state 1201, both clusters C1 and C2operate with conventional DIDO precoding independently and the client is served bycluster C1; in state 1202, the client is served by cluster C1, the BTS in C2 computesIDCI-precoding and cluster C1 operates using conventional DIDO precoding; in state1203, the client is served by cluster C2, the BTS in C1 computes IDCI-precoding andcluster C2 operates using conventional DIDO precoding; and in state 1204, the clientis served by cluster C2, and both clusters C1 and C2 operate with conventionalDIDO precoding independently.

In presence of shadowing effects, the signal quality or SIR mayfluctuate around the thresholds as shown in Figure 15, causing repetitive switchingbetween consecutive states in Figure 14. Changing states repetitively is anundesired effect, since it results in significant overhead on the control channelsbetween clients and BTSs to enable switching between transmission schemes.

Figure 15 depicts one example of a handoff strategy in the presence of shadowing.

In one embodiment, the shadowing coefficient is simulated according to log-normaldistribution with variance 3 [3]. Hereafter, we define some methods to preventrepetitive switching effect during DIDO handoff.

One embodiment of the invention employs a hysteresis loop to copewith state switching effects. For example, when switching between “C1-DIDO,C2-IDCI” 9302 and “C1-IDCI,C2-DIDO” 9303 states in Figure 14 (or vice versa) thethreshold SINR can be adjusted within the range A . This method avoids repetitiveT1 1switches between states as the signal quality oscillates around SINRT1. For example,Figure 16 shows the hysteresis loop mechanism when switching between any twostates in Figure 14. To switch from state B to A the SIR must be larger than(SIRT1+A1/2), but to switch back from A to B the SIR must drop below (SIRT1-A1/2).

In a different embodiment, the threshold SINRT2 is adjusted to avoidrepetitive switching between the first and second (or third and fourth) states of thefinite-state machine in Figure 14. For example, a range of values A may be definedsuch that the threshold SINRT2 is chosen within that range depending on channelcondition and shadowing effects.

In one embodiment, depending on the variance of shadowing expectedover the wireless link, the SINR threshold is dynamically adjusted within the range[SINRT2, SINRT2+A2]. The variance of the log-normal distribution can be estimatedfrom the variance of the received signal strength (or RSSI) as the client moves fromits current cluster to the neighbor cluster.

The methods above assume the client triggers the handoff strategy. Inone embodiment, the handoff decision is deferred to the DIDO BTSs, assumingcommunication across multiple BTSs is enabled.

For simplicity, the methods above are derived assuming no FECcoding and 4-QAM. More generally, the SINR or SIR thresholds are derived fordifferent modulation coding schemes (MCSs) and the handoff strategy is designed incombination with link adaptation (see, e.g., U.S. Patent No. 7,636,381) to optimizedownlink data rate to each client in the interfering zone.b. Handoff Between Low- and High-Doppler DIDO Networks DIDO systems employ closed-loop transmission schemes to precodedata streams over the downlink channel. Closed-loop schemes are inherentlyconstrained by latency over the feedback channel. In practical DIDO systems,computational time can be reduced by transceivers with high processing power andit is expected that most of the latency is introduced by the DIDO BSN, whendelivering CSI and baseband precoded data from the BTS to the distributedantennas. The BSN can be comprised of various network technologies including, butnot limited to, digital subscriber lines (DSL), cable modems, fiber rings, T1 lines,hybrid fiber coaxial (HFC) networks, and/or fixed wireless (e.g., WiFi). Dedicatedfiber typically has very large bandwidth and low latency, potentially less than amillisecond in local region, but it is less widely deployed than DSL and cablemodems. Today, DSL and cable modem connections typically have between 10-25ms in last-mile latency in the United States, but they are very widely deployed.

The maximum latency over the BSN determines the maximum Dopplerfrequency that can be tolerated over the DIDO wireless link without performancedegradation of DIDO precoding. For example, in [1] we showed that at the carrierfrequency of 400MHz, networks with latency of about 10msec (i.e., DSL) can tolerateclients’ velocity up to 8mph (running speed), whereas networks with 1msec latency(i.e., fiber ring) can support speed up to 70mph (i.e., freeway traffic).

We define two or multiple DIDO sub-networks depending on themaximum Doppler frequency that can be tolerated over the BSN. For example, aBSN with high-latency DSL connections between the DIDO BTS and distributedantennas can only deliver low mobility or fixed-wireless services (i.e., low-Dopplernetwork), whereas a low-latency BSN over a low-latency fiber ring can tolerate highmobility (i.e., high-Doppler network). We observe that the majority of broadbandusers are not moving when they use broadband, and further, most are unlikely to belocated near areas with many high speed objects moving by (e.g., next to a highway)since such locations are typically less desirable places to live or operate an office.

However, there are broadband users who will be using broadband at high speeds(e.g., while in a car driving on the highway) or will be near high speed objects (e.g.,in a store located near a highway). To address these two differing user Dopplerscenarios, in one embodiment, a low-Doppler DIDO network consists of a typicallylarger number of DIDO antennas with relatively low power (i.e., 1W to 100W, forindoor or rooftop installation) spread across a wide area, whereas a high-Dopplernetwork consists of a typically lower number of DIDO antennas with high powertransmission (i.e., 100W for rooftop or tower installation). The low-Doppler DIDOnetwork serves the typically larger number of low-Doppler users and can do so attypically lower connectivity cost using inexpensive high-latency broadbandconnections, such as DSL and cable modems. The high-Doppler DIDO networkserves the typically fewer number of high-Doppler users and can do so at typicallyhigher connectivity cost using more expensive low-latency broadband connections,such as fiber.

To avoid interference across different types of DIDO networks (e.g.low-Doppler and high-Doppler), different multiple access techniques can beemployed such as: time division multiple access (TDMA), frequency division multipleaccess (FDMA), or code division multiple access (CDMA).

Hereafter, we propose methods to assign clients to different types ofDIDO networks and enable handoff between them. The network selection is basedon the type of mobility of each client. The client’s velocity (v) is proportional to themaximum Doppler shift according to the following equation [6] = sin (11)where f is the maximum Doppler shift, is the wavelength corresponding to the carrierfrequency and is the angle between the vector indicating the direction transmitter-client and the velocity vector.

In one embodiment, the Doppler shift of every client is calculated viablind estimation techniques. For example, the Doppler shift can be estimated bysending RF energy to the client and analyzing the reflected signal, similar to Dopplerradar systems.

In another embodiment, one or multiple DIDO antennas send trainingsignals to the client. Based on those training signals, the client estimates the Dopplershift using techniques such as counting the zero-crossing rate of the channel gain, orperforming spectrum analysis. We observe that for fixed velocity v and client’strajectory, the angular velocity sin in (11) may depend on the relative distance ofthe client from every DIDO antenna. For example, DIDO antennas in the proximity ofa moving client yield larger angular velocity and Doppler shift than faraway antennas.

In one embodiment, the Doppler velocity is estimated from multiple DIDO antennasat different distances from the client and the average, weighted average or standarddeviation is used as an indicator for the client’s mobility. Based on the estimatedDoppler indicator, the DIDO BTS decides whether to assign the client to low- or high-Doppler networks.

The Doppler indicator is periodically monitored for all clients and sentback to the BTS. When one or multiple clients change their Doppler velocity (i.e.,client riding in the bus versus client walking or sitting), those clients are dynamicallyre-assigned to different DIDO network that can tolerate their level of mobility.

Although the Doppler of low-velocity clients can be affected by being inthe vicinity of high-velocity objects (e.g. near a highway), the Doppler is typically farless than the Doppler of clients that are in motion themselves. As such, in oneembodiment, the velocity of the client is estimated (e.g. by using a means such asmonitoring the clients position using GPS), and if the velocity is low, the client isassigned to a low-Doppler network, and if the velocity if high, the client is assigned toa high-Doppler network.

Methods for Power Control and Antenna Grouping The block diagram of DIDO systems with power control is depicted inFigure 17. One or multiple data streams (s ) for every client (1,…,U) are firstmultiplied by the weights generated by the DIDO precoding unit. Precoded datastreams are multiplied by power scaling factor computed by the power control unit,based on the input channel quality information (CQI). The CQI is either fed back fromthe clients to DIDO BTS or derived from the uplink channel assuming uplink-downlink channel reciprocity. The U precoded streams for different clients are thencombined and multiplexed into M data streams (t ), one for each of the M transmitantennas. Finally, the streams t are sent to the digital-to-analog converter (DAC)unit, the radio frequency (RF) unit, power amplifier (PA) unit and finally to theantennas.

The power control unit measures the CQI for all clients. In oneembodiment, the CQI is the average SNR or RSSI. The CQI varies for differentclients depending on pathloss or shadowing. Our power control method adjusts thetransmit power scaling factors P for different clients and multiplies them by theprecoded data streams generated for different clients. Note that one or multiple datastreams may be generated for every client, depending on the number of clients’receive antennas.

To evaluate the performance of the proposed method, we defined thefollowing signal model based on (5), including pathloss and power controlparameters = SNR P α + (12) where k=1,…,U, U is the number of clients, SNR=P /N , with P being the averageo o otransmit power, N the noise power and α the pathloss/shadowing coefficient. Tomodel pathloss/shadowing, we use the following simplified modelα = e (13)where a=4 is the pathloss exponent and we assume the pathloss increases with theclients’ index (i.e., clients are located at increasing distance from the DIDO antennas).

Figure 18 shows the SER versus SNR assuming four DIDO transmitantennas and four clients in different scenarios. The ideal case assumes all clientshave the same pathloss (i.e., a=0), yielding P =1 for all clients. The plot with squaresrefers to the case where clients have different pathloss coefficients and no powercontrol. The curve with dots is derived from the same scenario (with pathloss) wherethe power control coefficients are chosen such that = 1/α . With the powercontrol method, more power is assigned to the data streams intended to the clientsthat undergo higher pathloss/shadowing, resulting in 9dB SNR gain (for thisparticular scenario) compared to the case with no power control.

The Federal Communications Commission (FCC) (and otherinternational regulatory agencies) defines constraints on the maximum power thatcan be transmitted from wireless devices to limit the exposure of human body toelectromagnetic (EM) radiation. There are two types of limits [2]: i)“occupational/controlled” limit, where people are made fully aware of the radiofrequency (RF) source via fences, warnings or labels; ii) “generalpopulation/uncontrolled” limit where there is no control over the exposure.

Different emission levels are defined for different types of wirelessdevices. In general, DIDO distributed antennas used for indoor/outdoor applicationsqualify for the FCC category of “mobile” devices, defined as [2]:“transmitting devices designed to be used in other than fixed locations that wouldnormally be used with radiating structures maintained 20 cm or more from the body ofthe user or nearby persons.” The EM emission of “mobile” devices is measured in terms ofmaximum permissible exposure (MPE), expressed in mW/cm . Figure 19 shows theMPE power density as a function of distance from the source of RF radiation fordifferent values of transmit power at 700MHz carrier frequency. The maximumallowed transmit power to meet the FCC “uncontrolled” limit for devices that typicallyoperate beyond 20cm from the human body is 1W.

Less restrictive power emission constraints are defined for transmittersinstalled on rooftops or buildings, away from the “general population”. For these“rooftop transmitters” the FCC defines a looser emission limit of 1000W, measured interms of effective radiated power (ERP).

Based on the above FCC constraints, in one embodiment we definetwo types of DIDO distributed antennas for practical systems:• Low-power (LP) transmitters: located anywhere (i.e., indoor or outdoor) atany height, with maximum transmit power of 1W and 5Mbps consumer-gradebroadband (e.g. DSL, cable modem, Fibe To The Home (FTTH)) backhaulconnectivity.

• High-power (HP) transmitters: rooftop or building mounted antennas atheight of approximately 10 meters, with transmit power of 100W and a commercial-grade broadband (e.g. optical fiber ring) backhaul (with effectively “unlimited” data ratecompared to the throughput available over the DIDO wireless links).

Note that LP transmitters with DSL or cable modem connectivity aregood candidates for low-Doppler DIDO networks (as described in the previoussection), since their clients are mostly fixed or have low mobility. HP transmitters withcommercial fiber connectivity can tolerate higher client’s mobility and can be used inhigh-Doppler DIDO networks.

To gain practical intuition on the performance of DIDO systems withdifferent types of LP/HP transmitters, we consider the practical case of DIDOantenna installation in downtown Palo Alto, CA. Figure 20a shows a randomdistribution of NLP=100 low-power DIDO distributed antennas in Palo Alto. In Figure20b, 50 LP antennas are substituted with NHP=50 high-power transmitters.

Based on the DIDO antenna distributions in Figures 20a-b, we derivethe coverage maps in Palo Alto for systems using DIDO technology. Figures 21aand 21b show two power distributions corresponding to the configurations in Figure20a and Figure 20b, respectively. The received power distribution (expressed indBm) is derived assuming the pathloss/shadowing model for urban environmentsdefined by the 3GPP standard [3] at the carrier frequency of 700MHz. We observethat using 50% of HP transmitters yields better coverage over the selected area.

Figures 22a-b depict the rate distribution for the two scenarios above.

The throughput (expressed in Mbps) is derived based on power thresholds fordifferent modulation coding schemes defined in the 3GPP long-term evolution (LTE)standard in [4,5]. The total available bandwidth is fixed to 10MHz at 700MHz carrierfrequency. Two different frequency allocation plans are considered: i) 5MHzspectrum allocated only to the LP stations; ii) 9MHz to HP transmitters and 1MHz toLP transmitters. Note that lower bandwidth is typically allocated to LP stations due totheir DSL backhaul connectivity with limited throughput. Figures 22a-b shows thatwhen using 50% of HP transmitters it is possible to increase significantly the ratedistribution, raising the average per-client data rate from 2.4Mbps in Figure 22a to38Mbps in Figure 22b.

Next, we defined algorithms to control power transmission of LPstations such that higher power is allowed at any given time, thereby increasing thethroughput over the downlink channel of DIDO systems in Figure 22b. We observethat the FCC limits on the power density is defined based on average over time as∑ = (14)where = ∑ is the MPE averaging time, is the period of time of exposure to radiation with power density . For “controlled” exposure the average time is 6minutes, whereas for “uncontrolled” exposure it is increased up to 30 minutes. Then,any power source is allowed to transmit at larger power levels than the MPE limits, aslong as the average power density in (14) satisfies the FCC limit over 30 minuteaverage for “uncontrolled” exposure.

Based on this analysis, we define adaptive power control methods toincrease instantaneous per-antenna transmit power, while maintaining averagepower per DIDO antenna below MPE limits. We consider DIDO systems with moretransmit antennas than active clients. This is a reasonable assumption given thatDIDO antennas can be conceived as inexpensive wireless devices (similar to WiFiaccess points) and can be placed anywhere there is DSL, cable modem, opticalfiber, or other Internet connectivity.

The framework of DIDO systems with adaptive per-antenna powercontrol is depicted in Figure 23. The amplitude of the digital signal coming out of themultiplexer 234 is dynamically adjusted with power scaling factors S ,…,S , beforebeing sent to the DAC units 235. The power scaling factors are computed by thepower control unit 232 based on the CQI 233.

In one embodiment, N DIDO antenna groups are defined. Every groupcontains at least as many DIDO antennas as the number of active clients (K). At anygiven time, only one group has N >K active DIDO antennas transmitting to the clients at larger power level (S ) than MPE limit ( ). One method iterates acrossall antenna groups according to Round-Robin scheduling policy depicted in Figure24. In another embodiment, different scheduling techniques (i.e., proportional-fairscheduling [8]) are employed for cluster selection to optimize error rate or throughputperformance.

Assuming Round-Robin power allocation, from (14) we derive theaverage transmit power for every DIDO antenna as = ≤ (15)where to is the period of time over which the antenna group is active and TMPE=30minis the average time defined by the FCC guidelines [2]. The ratio in (15) is the dutyfactor (DF) of the groups, defined such that the average transmit power from every DIDO antenna satisfies the MPE limit ( ). The duty factor depends on the numberof active clients, the number of groups and active antennas per-group, according tothe following definition ≜ = . (16) The SNR gain (in dB) obtained in DIDO systems with power control and antennagrouping is expressed as a function of the duty factor as = 10 log . (17) We observe the gain in (17) is achieved at the expense of GdB additional transmitpower across all DIDO antennas.

In general, the total transmit power from all N of all N groups is defined as = ∑ ∑ (18) where the P is the average per-antenna transmit power given by = ( ) ≤ (19) th thand S (t) is the power spectral density for the i transmit antenna within the j group.

In one embodiment, the power spectral density in (19) is designed for every antennato optimize error rate or throughput performance.

To gain some intuition on the performance of the proposed method,consider 400 DIDO distributed antennas in a given coverage area and 400 clientssubscribing to a wireless Internet service offered over DIDO systems. It is unlikelythat every Internet connection will be fully utilized all the time. Let us assume that% of the clients will be actively using the wireless Internet connection at any giventime. Then, 400 DIDO antennas can be divided in N =10 groups of N =40 antennaseach, every group serving K=40 active clients at any given time with duty factorDF=0.1. The SNR gain resulting from this transmission scheme isGdB=10log10(1/DF)=10dB, provided by 10dB additional transmit power from all DIDOantennas. We observe, however, that the average per-antenna transmit power isconstant and is within the MPE limit.

Figure 25 compares the (uncoded) SER performance of the abovepower control with antenna grouping against conventional eigenmode selection inU.S. Patent No. 7,636,381. All schemes use BD precoding with four clients, eachclient equipped with single antenna. The SNR refers to the ratio of per-transmit-antenna power over noise power (i.e., per-antenna transmit SNR). The curvedenoted with DIDO 4x4 assumes four transmit antenna and BD precoding. Thecurve with squares denotes the SER performance with two extra transmit antennasand BD with eigenmode selection, yielding 10dB SNR gain (at 1% SER target) overconventional BD precoding. Power control with antenna grouping and DF=1/10yields 10dB gain at the same SER target as well. We observe that eigenmodeselection changes the slope of the SER curve due to diversity gain, whereas ourpower control method shifts the SER curve to the left (maintaining the same slope)due to increased average transmit power. For comparison, the SER with larger dutyfactor DF=1/50 is shown to provide additional 7dB gain compared to DF=1/10.

Note that our power control may have lower complexity thanconventional eigenmode selection methods. In fact, the antenna ID of every groupcan be pre-computed and shared among DIDO antennas and clients via lookuptables, such that only K channel estimates are required at any given time. Foreigenmode selection, (K+2) channel estimates are computed and additionalcomputational processing is required to select the eigenmode that minimizes theSER at any given time for all clients.

Next, we describe another method involving DIDO antenna grouping toreduce CSI feedback overhead in some special scenarios. Figure 26a shows onescenario where clients (dots) are spread randomly in one area covered by multipleDIDO distributed antennas (crosses). The average power over every transmit-receive wireless link can be computed as| | = . (20)where H is the channel estimation matrix available at the DIDO BTS.

The matrices A in Figures 26a-c are obtained numerically byaveraging the channel matrices over 1000 instances. Two alternative scenarios aredepicted in Figure 26b and Figure 26c, respectively, where clients are groupedtogether around a subset of DIDO antennas and receive negligible power from DIDOantennas located far away. For example, Figure 26b shows two groups of antennasyielding block diagonal matrix A. One extreme scenario is when every client is veryclose to only one transmitter and the transmitters are far away from one another,such that the power from all other DIDO antennas is negligible. In this case, theDIDO link degenerates in multiple SISO links and A is a diagonal matrix as in Figure26c.

In all three scenarios above, the BD precoding dynamically adjusts theprecoding weights to account for different power levels over the wireless linksbetween DIDO antennas and clients. It is convenient, however, to identify multiplegroups within the DIDO cluster and operate DIDO precoding only within each group.

Our proposed grouping method yields the following advantages:• Computational gain: DIDO precoding is computed only within every groupin the cluster. For example, if BD precoding is used, singular value decomposition(SVD) has complexity O(n ), where n is the minimum dimension of the channel matrixH. If H can be reduced to a block diagonal matrix, the SVD is computed for every blockwith reduced complexity. In fact, if the channel matrix is divided into two block matriceswith dimensions n1 and n2 such that n=n1+n2, the complexity of the SVD is only3 3 3O(n )+O(n )<O(n ). In the extreme case, if H is diagonal matrix, the DIDO link reduceto multiple SISO links and no SVD calculation is required.

• Reduced CSI feedback overhead: When DIDO antennas and clients aredivided into groups, in one embodiment, the CSI is computed from the clients to theantennas only within the same group. In TDD systems, assuming channel reciprocity,antenna grouping reduces the number of channel estimates to compute the channelmatrix H. In FDD systems where the CSI is fed back over the wireless link, antennagrouping further yields reduction of CSI feedback overhead over the wireless linksbetween DIDO antennas and clients.

Multiple Access Techniques for the DIDO Uplink Channel In one embodiment of the invention, different multiple accesstechniques are defined for the DIDO uplink channel. These techniques can be usedto feedback the CSI or transmit data streams from the clients to the DIDO antennasover the uplink. Hereafter, we refer to feedback CSI and data streams as uplinkstreams.

• Multiple-input multiple-output (MIMO): the uplink streams aretransmitted from the client to the DIDO antennas via open-loop MIMO multiplexingschemes. This method assumes all clients are time/frequency synchronized. In oneembodiment, synchronization among clients is achieved via training from the downlinkand all DIDO antennas are assumed to be locked to the same time/frequencyreference clock. Note that variations in delay spread at different clients may generatejitter between the clocks of different clients that may affect the performance of MIMOuplink scheme. After the clients send uplink streams via MIMO multiplexing schemes,the receive DIDO antennas may use non-linear (i.e., maximum likelihood, ML) or linear(i.e., zeros-forcing, minimum mean squared error) receivers to cancel co-channelinterference and demodulate the uplink streams individually.

• Time division multiple access (TDMA): Different clients are assigned todifferent time slots. Every client sends its uplink stream when its time slot is available.

• Frequency division multiple access (FDMA): Different clients areassigned to different carrier frequencies. In multicarrier (OFDM) systems, subsets oftones are assigned to different clients that transmit the uplink streams simultaneously,thereby reducing latency.

• Code division multiple access (CDMA): Every client is assigned to adifferent pseudo-random sequence and orthogonality across clients is achieved in thecode domain.

In one embodiment of the invention, the clients are wireless devicesthat transmit at much lower power than the DIDO antennas. In this case, the DIDOBTS defines client sub-groups based on the uplink SNR information, such thatinterference across sub-groups is minimized. Within every sub-group, the abovemultiple access techniques are employed to create orthogonal channels in time,frequency, space or code domains thereby avoiding uplink interference acrossdifferent clients.

In another embodiment, the uplink multiple access techniquesdescribed above are used in combination with antenna grouping methods presentedin the previous section to define different client groups within the DIDO cluster.

System and Method for Link Adaptation in DIDO Multicarrier Systems Link adaptation methods for DIDO systems exploiting time, frequencyand space selectivity of wireless channels were defined in U.S. Patent No.7,636,381. Described below are embodiments of the invention for link adaptation inmulticarrier (OFDM) DIDO systems that exploit time/frequency selectivity of wirelesschannels.

We simulate Rayleigh fading channels according to the exponentiallydecaying power delay profile (PDP) or Saleh-Valenzuela model in [9]. For simplicity,we assume single-cluster channel with multipath PDP defined as = (21)where n=0,…,L-1, is the index of the channel tap, L is the number of channel taps and = 1/ is the PDP exponent that is an indicator of the channel coherencebandwidth, inverse proportional to the channel delay spread ( ). Low values of yield frequency-flat channels, whereas high values of produce frequency selectivechannels. The PDP in (21) is normalized such that the total average power for all Lchannel taps is unitary = . (22)Figure 27 depicts the amplitude of low frequency selective channels (assuming =1) over delay domain or instantaneous PDP (upper plot) and frequency domain (lowerplot) for DIDO 2x2 systems. The first subscript indicates the client, the secondsubscript the transmit antenna. High frequency selective channels (with = 0.1 ) areshown in Figure 28.

Next, we study the performance of DIDO precoding in frequencyselective channels. We compute the DIDO precoding weights via BD, assuming thesignal model in (1) that satisfies the condition in (2). We reformulate the DIDOreceive signal model in (5), with the condition in (2), as = + . (23) where = is the effective channel matrix for user k. For DIDO 2x2, with a single antenna per client, the effective channel matrix reduces to onevalue with a frequency response shown in Figure 29 and for channels characterizedby high frequency selectivity (e.g., with = 0.1 ) in Figure 28. The continuous line inFigure 29 refers to client 1, whereas the line with dots refers to client 2. Based onthe channel quality metric in Figure 29 we define time/frequency domain linkadaptation (LA) methods that dynamically adjust MCSs, depending on the changingchannel conditions.

We begin by evaluating the performance of different MCSs in AWGNand Rayleigh fading SISO channels. For simplicity, we assume no FEC coding, butthe following LA methods can be extended to systems that include FEC.

Figure 30 shows the SER for different QAM schemes (i.e., 4-QAM, 16-QAM, 64-QAM). Without loss of generality, we assume target SER of 1% foruncoded systems. The SNR thresholds to meet that target SER in AWGN channelsare 8dB, 15.5dB and 22dB for the three modulation schemes, respectively. InRayleigh fading channels, it is well known the SER performance of the abovemodulation schemes is worse than AWGN [13] and the SNR thresholds are: 18.6dB,27.3dB and 34.1dB, respectively. We observe that DIDO precoding transforms themulti-user downlink channel into a set of parallel SISO links. Hence, the same SNRthresholds as in Figure 30 for SISO systems hold for DIDO systems on a client-by-client basis. Moreover, if instantaneous LA is carried out, the thresholds in AWGNchannels are used.

The key idea of the proposed LA method for DIDO systems is to uselow MCS orders when the channel undergoes deep fades in the time domain orfrequency domain (depicted in Figure 28) to provide link-robustness. Contrarily,when the channel is characterized by large gain, the LA method switches to higherMCS orders to increase spectral efficiency. One contribution of the presentapplication compared to U.S. Patent No. 7,636,381 is to use the effective channelmatrix in (23) and in Figure 29 as a metric to enable adaptation.

The general framework of the LA methods is depicted in Figure 31 anddefined as follows:• CSI estimation: At 3171 the DIDO BTS computes the CSI from all users.

Users may be equipped with single or multiple receive antennas.

• DIDO precoding: At 3172, the BTS computes the DIDO precoding weightsfor all users. In one embodiment, BD is used to compute these weights. The precodingweights are calculated on a tone-by-tone basis.

• Link-quality metric calculation: At 3173 the BTS computes thefrequency-domain link quality metrics. In OFDM systems, the metrics are calculatedfrom the CSI and DIDO precoding weights for every tone. In one embodiment of theinvention, the link-quality metric is the average SNR over all OFDM tones. We definethis method as LA1 (based on average SNR performance). In another embodiment,the link quality metric is the frequency response of the effective channel in (23). Wedefine this method as LA2 (based on tone-by-tone performance to exploit frequencydiversity). If every client has single antenna, the frequency-domain effective channelis depicted in Figure 29. If the clients have multiple receive antennas, the link-qualitymetric is defined as the Frobenius norm of the effective channel matrix for every tone.

Alternatively, multiple link-quality metrics are defined for every client as the singularvalues of the effective channel matrix in (23).

• Bit-loading algorithm: At 3174, based on the link-quality metrics, the BTSdetermines the MCSs for different clients and different OFDM tones. For LA1 method,the same MCS is used for all clients and all OFDM tones based on the SNR thresholdsfor Rayleigh fading channels in Figure 30. For LA2, different MCSs are assigned todifferent OFDM tones to exploit channel frequency diversity.

• Precoded data transmission: At 3175, the BTS transmits precoded datastreams from the DIDO distributed antennas to the clients using the MCSs derivedfrom the bit-loading algorithm. One header is attached to the precoded data tocommunicate the MCSs for different tones to the clients. For example, if eight MCSsare available and the OFDM symbols are defined with N=64 tone, log2(8)*N=192 bitsare required to communicate the current MCS to every client. Assuming 4-QAM (2bits/symbol spectral efficiency) is used to map those bits into symbols, only192/2/N=1.5 OFDM symbols are required to map the MCS information. In anotherembodiment, multiple subcarriers (or OFDM tones) are grouped into subbands andthe same MCS is assigned to all tones in the same subband to reduce the overheaddue to control information. Moreover, the MCS are adjusted based on temporalvariations of the channel gain (proportional to the coherence time). In fixed-wirelesschannel (characterized by low Doppler effect) the MCS are recalculated every fractionof the channel coherence time, thereby reducing the overhead required for controlinformation.

Figure 32 shows the SER performance of the LA methods describedabove. For comparison, the SER performance in Rayleigh fading channels is plottedfor each of the three QAM schemes used. The LA2 method adapts the MCSs to thefluctuation of the effective channel in the frequency domain, thereby providing1.8bps/Hz gain in spectral efficiency for low SNR (i.e., SNR=20dB) and 15dB gain inSNR (for SNR>35dB) compared to LA1.

System and Method for DIDO Precoding Interpolation in Multicarrier Systems The computational complexity of DIDO systems is mostly localized atthe centralized processor or BTS. The most computationally expensive operation isthe calculation of the precoding weights for all clients from their CSI. When BDprecoding is employed, the BTS has to carry out as many singular valuedecomposition (SVD) operations as the number of clients in the system. One way toreduce complexity is through parallelized processing, where the SVD is computed ona separate processor for every client.

In multicarrier DIDO systems, each subcarrier undergoes flat-fadingchannel and the SVD is carried out for every client over every subcarrier. Clearly thecomplexity of the system increases linearly with the number of subcarriers. Forexample, in OFDM systems with 1MHz signal bandwidth, the cyclic prefix (L ) musthave at least eight channel taps (i.e., duration of 8 microseconds) to avoidintersymbol interference in outdoor urban macrocell environments with large delayspread [3]. The size (N ) of the fast Fourier transform (FFT) used to generate theOFDM symbols is typically set to multiple of L to reduce loss of data rate. IfNFFT=64, the effective spectral efficiency of the system is limited by a factor NFFT/(N +L )=89%. Larger values of N yield higher spectral efficiency at the expenseFFT 0 FFTof higher computational complexity at the DIDO precoder.

One way to reduce computational complexity at the DIDO precoder isto carry out the SVD operation over a subset of tones (that we call pilot tones) andderive the precoding weights for the remaining tones via interpolation. Weightinterpolation is one source of error that results in inter-client interference. In oneembodiment, optimal weight interpolation techniques are employed to reduce inter-client interference, yielding improved error rate performance and lowercomputational complexity in multicarrier systems. In DIDO systems with M transmitantennas, U clients and N receive antennas per clients, the condition for theprecoding weights of the k client ( ) that guarantees zero interference to the otherclients u is derived from (2) as = ; ∀ = 1, … , ; with ≠ (24)where are the channel matrices corresponding to the other DIDO clients in thesystem.

In one embodiment of the invention, the objective function of the weightinterpolation method is defined asf( ) = ∑ ( ) (25) where is the set of parameters to be optimized for user k, ( ) is the weight interpolation matrix and ‖∙‖ denotes the Frobenius norm of a matrix. The optimizationproblem is formulated as = arg min f( ) (26), ϵ Θwhere Θ is the feasible set of the optimization problem and is the optimal ,solution.

The objective function in (25) is defined for one OFDM tone. In anotherembodiment of the invention, the objective function is defined as linear combinationof the Frobenius norm in (25) of the matrices for all the OFDM tones to beinterpolated. In another embodiment, the OFDM spectrum is divided into subsets oftones and the optimal solution is given by = arg min max f(n, ) (27), ϵ Θ ϵwhere n is the OFDM tone index and A is the subset of tones.

The weight interpolation matrix ( ) in (25) is expressed as afunction of a set of parameters . Once the optimal set is determined according to(26) or (27), the optimal weight matrix is computed. In one embodiment of theinvention, the weight interpolation matrix of given OFDM tone n is defined as linearcombination of the weight matrices of the pilot tones. One example of weightinterpolation function for beamforming systems with single client was defined in [11].

In DIDO multi-client systems we write the weight interpolation matrix as ( + , ) = (1 − ) ∙ ( ) + e ∙ ( + 1 ) (28) where 0 ≤ ≤ ( -1), L is the number of pilot tones and = ( − 1)/ , with = / . The weight matrix in (28) is then normalized such that = to guarantee unitary power transmission from every antenna. If N=1 (single receiveantenna per client), the matrix in (28) becomes a vector that is normalized with respectto its norm. In one embodiment of the invention, the pilot tones are chosen uniformlywithin the range of the OFDM tones. In another embodiment, the pilot tones areadaptively chosen based on the CSI to minimize the interpolation error.

We observe that one key difference of the system and method in [11]against the one proposed in this patent application is the objective function. Inparticular, the systems in [11] assumes multiple transmit antennas and single client,so the related method is designed to maximize the product of the precoding weightby the channel to maximize the receive SNR for the client. This method, however,does not work in multi-client scenarios, since it yields inter-client interference due tointerpolation error. By contrast, our method is designed to minimize inter-clientinterference thereby improving error rate performance to all clients.

Figure 33 shows the entries of the matrix in (28) as a function of theOFDM tone index for DIDO 2x2 systems with = 64 and = 8. The channel PDP is generated according to the model in (21) with = 1 and the channel consistsof only eight channel taps. We observe that L must be chosen to be larger than thenumber of channel taps. The solid lines in Figure 33 represent the ideal functions,whereas the dotted lines are the interpolated ones. The interpolated weights matchthe ideal ones for the pilot tones, according to the definition in (28). The weightscomputed over the remaining tones only approximate the ideal case due toestimation error.

One way to implement the weight interpolation method is viaexhaustive search over the feasible set Θ in (26). To reduce the complexity of thesearch, we quantize the feasible set into P values uniformly in the range [0,2 ].

Figure 34 shows the SER versus SNR for = 8, M=Nt=2 transmit antennas andvariable number of P. As the number of quantization levels increases, the SERperformance improves. We observe the case P=10 approaches the performance ofP=100 for much lower computational complexity, due to reduced number ofsearches.

Figure 35 shows the SER performance of the interpolation method fordifferent DIDO orders and = 16. We assume the number of clients is the same asthe number of transmit antennas and every client is equipped with single antenna.

As the number of clients increases the SER performance degrades due to increaseinter-client interference produced by weight interpolation errors.

In another embodiment of the invention, weight interpolation functionsother than those in (28) are used. For example, linear prediction autoregressivemodels [12] can be used to interpolate the weights across different OFDM tones,based on estimates of the channel frequency correlation.

References [1] A. Forenza and S. G. Perlman, “System and method for distributedantenna wireless communications”, U.S. Application Serial No. 12/630,627, filedDecember 2, 2009, entitled ”System and Method For Distributed Antenna WirelessCommunications” [2] FCC, “Evaluating compliance with FCC guidelines for humanexposure to radiofrequency electromagnetic fields,” OET Bulletin 65, Ed. 97-01, Aug.1997 [3] 3GPP, “Spatial Channel Model AHG (Combined ad-hoc from 3GPP& 3GPP2)”, SCM Text V6.0, April 22, 2003 [4] 3GPP TR 25.912, “Feasibility Study for Evolved UTRA andUTRAN”, V9.0.0 (2009-10) [5] 3GPP TR 25.913, “Requirements for Evolved UTRA (E-UTRA) andEvolved UTRAN (E-UTRAN)”, V8.0.0 (2009-01) [6] W. C. Jakes, Microwave Mobile Communications, IEEE Press, 1974 [7] K. K. Wong, et al., "A joint channel diagonalization for multiuserMIMO antenna systems," IEEE Trans. Wireless Comm., vol. 2, pp. 773-786, July2003; [8] P. Viswanath, et al., “Opportunistic beamforming using dumpantennas,” IEEE Trans. On Inform. Theory, vol. 48, pp. 1277–1294, June 2002. [9] A. A. M. Saleh, et al., “A statistical model for indoor multipathpropagation,” IEEE Jour. Select. Areas in Comm., vol. 195 SAC-5, no. 2, pp. 128–137, Feb. 1987.

[10] A. Paulraj, et al., Introduction to Space-Time WirelessCommunications, Cambridge University Press, 40 West 20th Street, New York, NY,USA, 2003.

[11] J. Choi, et al., ``Interpolation Based Transmit Beamforming forMIMO-OFDM with Limited Feedback,'' IEEE Trans. on Signal Processing, vol. 53,no. 11, pp. 4125-4135, Nov. 2005.

[12] I. Wong, et al., ``Long Range Channel Prediction for AdaptiveOFDM Systems,'' Proc. of the IEEE Asilomar Conf. on Signals, Systems, andComputers, vol. 1,pp. 723-736, Pacific Grove, CA, USA, Nov. 7-10, 2004.

[13] J. G. Proakis, Communication System Engineering, Prentice Hall,1994

[14] B.D.Van Veen, et al., ``Beamforming: a versatile approach tospatial filtering,'' IEEE ASSP Magazine, Apr. 1988.

[15] R.G. Vaughan, “On optimum combining at the mobile,” IEEETrans. On Vehic. Tech., vol37, n.4, pp.181-188, Nov. 1988

[16] F.Qian, “Partially adaptive beamforming for correlated interferencerejection,” IEEE Trans. On Sign. Proc., vol.43, n.2, pp.506-515, Feb.1995

[17] H.Krim, et. al., “Two decades of array signal processing research,”IEEE Signal Proc. Magazine, pp.67-94, July 1996

[19] W.R. Remley, “Digital beamforming system”, US Patent N.4,003,016, Jan. 1977

[18] R.J. Masak, “Beamforming/null-steering adaptive array”, US PatentN. 4,771,289, Sep.1988

[20] K.-B.Yu, et. al., “Adaptive digital beamforming architecture andalgorithm for nulling mainlobe and multiple sidelobe radar jammers while preservingmonopulse ratio angle estimation accuracy”, US Patent 5,600,326, Feb.1997

[21] H.Boche, et al., “Analysis of different precoding/decodingstrategies for multiuser beamforming”, IEEE Vehic. Tech. Conf., vol.1, Apr. 2003

[22] M.Schubert, et al., “Joint 'dirty paper' pre-coding and downlinkbeamforming,” vol.2, pp.536-540, Dec. 2002

[23] H.Boche, et al.” A general duality theory for uplink and downlinkbeamformingc”, vol.1, pp.87-91, Dec. 2002

[24] K. K. Wong, R. D. Murch, and K. B. Letaief, “A joint channeldiagonalization for multiuser MIMO antenna systems,” IEEE Trans. Wireless Comm.,vol. 2, pp. 773–786, Jul 2003;

[25] Q. H. Spencer, A. L. Swindlehurst, and M. Haardt, “Zero forcingmethods for downlink spatial multiplexing in multiuser MIMO channels,” IEEE Trans.

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II. DISCLOSURE FROM RELATED APPLICATION SERIAL NO.12/917,257 Described below are wireless radio frequency (RF) communicationsystems and methods employing a plurality of distributed transmitting antennasoperating cooperatively to create wireless links to given users, while suppressinginterference to other users. Coordination across different transmitting antennas isenabled via user-clustering. The user cluster is a subset of transmitting antennaswhose signal can be reliably detected by given user (i.e., received signal strengthabove noise or interference level). Every user in the system defines its own user-cluter. The waveforms sent by the transmitting antennas within the same user-clustercoherently combine to create RF energy at the target user’s location and points ofzero RF interference at the location of any other user reachable by those antennas.

Consider a system with M transmit antennas within one user-clusterand K users reachable by those M antennas, with ≤ M . We assume thetransmitters are aware of the CSI ( ∈ ∁ ) between the M transmit antennas and Kusers. For simplicity, every user is assumed to be equipped with a single antenna,but the same method can be extended to multiple receive antennas per user.

Consider the channel matrix H obtained by combining the channel vectors ( ∈∁ ) from the M transmit antennas to the K users as = .

The precoding weights ( ∈ ∁ ) that create RF energy to user k and zero RFenergy to all other K-1 users are computed to satisfy the following condition = where is the effective channel matrix of user k obtained by removing the k-th rowof matrix H and 0 is the vector with all zero entries In one embodiment, the wireless system is a DIDO system and userclustering is employed to create a wireless communication link to the target user,while pre-cancelling interference to any other user reachable by the antennas lyingwithin the user-cluster. In U.S. Application Serial No. 12/630,627, a DIDO system isdescribed which includes:• DIDO clients: user terminals equipped with one or multiple antennas;• DIDO distributed antennas: transceiver stations operating cooperativelyto transmit precoded data streams to multiple users, thereby suppressing inter-userinterference;• DIDO base transceiver stations (BTS): centralized processor generatingprecoded waveforms to the DIDO distributed antennas;• DIDO base station network (BSN): wired backhaul connecting the BTS tothe DIDO distributed antennas or to other BTSs.

The DIDO distributed antennas are grouped into different subsets depending on theirspatial distribution relative to the location of the BTSs or DIDO clients. We define threetypes of clusters, as depicted in Figure 36:• Super-cluster 3640: is the set of DIDO distributed antennas connected toone or multiple BTSs such that the round-trip latency between all BTSs and therespective users is within the constraint of the DIDO precoding loop;• DIDO-cluster 3641: is the set of DIDO distributed antennas connected tothe same BTS. When the super-cluster contains only one BTS, its definition coincideswith the DIDO-cluster;• User-cluster 3642: is the set of DIDO distributed antennas thatcooperatively transmit precoded data to given user.

For example, the BTSs are local hubs connected to other BTSs and tothe DIDO distributed antennas via the BSN. The BSN can be comprised of variousnetwork technologies including, but not limited to, digital subscriber lines (DSL),ADSL, VDSL [6], cable modems, fiber rings, T1 lines, hybrid fiber coaxial (HFC)networks, and/or fixed wireless (e.g., WiFi). All BTSs within the same super-clustershare information about DIDO precoding via the BSN such that the round-trip latencyis within the DIDO precoding loop.

In Figure 37, the dots denote DIDO distributed antennas, the crossesare the users and the dashed lines indicate the user-clusters for users U1 and U8,respectively. The method described hereafter is designed to create a communicationlink to the target user U1 while creating points of zero RF energy to any other user(U2-U8) inside or outside the user-cluster.

We proposed similar method in [5], where points of zero RF energywere created to remove interference in the overlapping regions between DIDOclusters. Extra antennas were required to transmit signal to the clients within theDIDO cluster while suppressing inter-cluster interference. One embodiment of amethod proposed in the present application does not attempt to remove inter-DIDO-cluster interference; rather it assumes the cluster is bound to the client (i.e., user-cluster) and guarantees that no interference (or negligible interference) is generatedto any other client in that neighborhood.

One idea associated with the proposed method is that users farenough from the user-cluster are not affected by radiation from the transmitantennas, due to large pathloss. Users close or within the user-cluster receiveinterference-free signal due to precoding. Moreover, additional transmit antennascan be added to the user-cluster (as shown in Figure 37) such that the condition ≤ is satisfied.

One embodiment of a method employing user clustering consists of thefollowing steps:a. Link-quality measurements: the link quality between every DIDOdistributed antenna and every user is reported to the BTS. The link-quality metricconsists of signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio(SINR).

In one embodiment, the DIDO distributed antennas transmit training signals and theusers estimate the received signal quality based on that training. The training signalsare designed to be orthogonal in time, frequency or code domains such that the userscan distinguish across different transmitters. Alternatively, the DIDO antennas transmitnarrowband signals (i.e., single tone) at one particular frequency (i.e., a beaconchannel) and the users estimate the link-quality based on that beacon signal. Onethreshold is defined as the minimum signal amplitude (or power) above the noise levelto demodulate data successfully as shown in Figure 38a. Any link-quality metric valuebelow this threshold is assumed to be zero. The link-quality metric is quantized over afinite number of bits and fed back to the transmitter.

In a different embodiment, the training signals or beacons are sent from the users andthe link quality is estimated at the DIDO transmit antennas (as in Figure 38b),assuming reciprocity between uplink (UL) and downlink (DL) pathloss. Note thatpathloss reciprocity is a realistic assumption in time division duplexing (TDD) systems(with UL and DL channels at the same frequency) and frequency division duplexing(FDD) systems when the UL and DL frequency bands are reatively close.

Information about the link-quality metrics is shared across different BTSs through theBSN as depicted in Figure 37 such that all BTSs are aware of the link-quality betweenevery antenna/user couple across different DIDO clusters.b. Definition of user-clusters: the link-quality metrics of all wireless links inthe DIDO clusters are the entries to the link-quality matrix shared across all BTSs viathe BSN. One example of link-quality matrix for the scenario in Figure 37 is depictedin Figure 39.

The link-quality matrix is used to define the user clusters. For example, Figure 39shows the selection of the user cluster for user U8. The subset of transmitters withnon-zero link-quality metrics (i.e., active transmitters) to user U8 is first identified.

These transmitters populate the user-cluster for the user U8. Then the sub-matrixcontaining non-zero entries from the transmitters within the user-cluster to the otherusers is selected. Note that since the link-quality metrics are only used to select theuser cluster, they can be quantized with only two bits (i.e., to identify the state aboveor below the thresholds in Figure 38) thereby reducing feedback overhead.

Another example is depicted in Figure 40 for user U1. In this case thenumber of active transmitters is lower than the number of users in the sub-matrix,thereby violating the condition ≤ M . Therefore, one or more columns are added tothe sub-matrix to satisfy that condition. If the number of transmitters exceeds thenumber of users, the extra antennas can be used for diversity schemes (i.e., antennaor eigenmode selection).

Yet another example is shown in Figure 41 for user U4. We observethat the sub-matrix can be obtained as combination of two sub-matrices.c. CSI report to the BTSs: Once the user clusters are selected, the CSI fromall transmitters within the user-cluster to every user reached by those transmitters ismade available to all BTSs. The CSI information is shared across all BTSs via theBSN. In TDD systems, UL/DL channel reciprocity can be exploited to derive the CSIfrom training over the UL channel. In FDD systems, feedback channels from all usersto the BTSs are required. To reduce the amount of feedback, only the CSIcorresponding to the non-zero entries of the link-quality matrix are fed back.d. DIDO precoding: Finally, DIDO precoding is applied to every CSI sub-matrix corresponding to different user clusters (as described, for example, in therelated U.S. Patent Applications).

In one embodiment, singular value decomposition (SVD) of the effective channelmatrix is computed and the precoding weight for user k is defined as the rightsigular vector corresponding to the null subspace of . Alternatively, if M>K and theSVD decomposes the effective channel matrix as = , the DIDO precoding weight for user k is given by = ( ∙ ) where is the matrix with columns being the singular vectors of the null subspace ofFrom basic linear algebra considerations, we observe that the right singular vector inthe null subspace of the matrix is equal to the eigenvetor of C corresponding to thezero eigenvalue ( ) ( ) = = = where the effective channel matrix is decomposed as = , according to theSVD. Then, one alternative to computing the SVD of is to calculate the eigenvaluedecomposition of C. There are several methods to compute eigenvalue decompositionsuch as the power method. Since we are only interested to the eigenvectorcorresponding to the null subspace of C, we use the inverse power method describedby the iteration( − ) ( − ) where the vector ( ) at the first iteration is a random vector.

Given that the eigenvalue ( ) of the null subspace is known (i.e., zero) the inversepower method requires only one iteration to converge, thereby reducing computationalcomplexity. Then, we write the precoding weight vector as = where is the vector with real entries equal to 1 (i.e., the precoding weight vector isthe sum of the columns of ).

The DIDO precoding calculation requires one matrix inversion. There are severalnumerical solutions to reduce the complexity of matrix inversions such as theStrassen’s algorithm [1] or the Coppersmith-Winograd’s algorithm [2,3]. Since C isHermitian matrix by definition, an alternative solution is to decompose C in its real andimaginary components and compute matrix inversion of a real matrix, according to themethod in [4, Section 11.4].

Another feature of the proposed method and system is itsreconfigurability. As the client moves across different DIDO clusters as in Figure 42,the user-cluster follows its moves. In other words, the subset of transmit antennas isconstantly updated as the client changes its position and the effective channel matrix(and corresponding precoding weights) are recomputed.

The method proposed herein works within the super-cluster in Figure36, since the links between the BTSs via the BSN must be low-latency. To suppressinterference in the overlapping regions of different super-clusters, it is possible to useour method in [5] that uses extra antennas to create points of zero RF energy in theinterfering regions between DIDO clusters.

It should be noted that the terms “user” and “client” are usedinterchangeably herein.

References [1] S. Robinson, “Toward an Optimal Algorithm for MatrixMultiplication”, SIAM News, Volume 38, Number 9, November 2005. [2] D. Coppersmith and S. Winograd, “Matrix Multiplication viaArithmetic Progression”, J. Symb. Comp. vol.9, p.251-280, 1990. [3] H. Cohn, R. Kleinberg, B. Szegedy, C. Umans, “Group-theoreticAlgorithms for Matrix Multiplication”, p. 379-388, Nov. 2005. [4] W.H. Press, S.A. Teukolsky, W. T. Vetterling, B.P. Flannery“NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING”,Cambridge University Press, 1992. [5] A. Forenza and S.G.Perlman, “INTERFERENCE MANAGEMENT,HANDOFF, POWER CONTROL AND LINK ADAPTATION IN DISTRIBUTED-INPUT DISTRIBUTED-OUTPUT (DIDO) COMMUNICATION SYSTEMS”, Patent Application Serial No. 12/802,988,filed June 16, 2010. [6] Per-Erik Eriksson and Björn Odenhammar, “VDSL2: Next importantbroadband technology”, Ericsson Review No. 1, 2006.

III. SYSTEMS AND METHODS TO EXPLOIT AREAS OF COHERENCE INWIRELESS SYSTEMS The capacity of multiple antenna systems (MAS) in practicalpropagation environments is a function of the spatial diversity available over thewireless link. Spatial diversity is determined by the distribution of scattering objectsin the wireless channel as well as the geometry of transmit and receive antennaarrays.

One popular model for MAS channels is the so called clusteredchannel model, that defines groups of scatterers as clusters located around thetransmitters and receivers. In general, the more clusters and the larger their angularspread, the higher spatial diversity and capacity achievable over wireless links.

Clustered channel models have been validated through practical measurements [1-2] and variations of those models have been adopted by different indoor (i.e., IEEE802.11n Technical Group [3] for WLAN) and outdoor (3GPP Technical SpecificationGroup for 3G cellular systems [4]) wireless standards.

Other factors that determine the spatial diversity in wireless channelsare the characteristics of the antenna arrays, including: antenna element spacing [5-7], number of antennas [8-9], array aperture [10-11], array geometry [5,12,13],polarization and antenna pattern [14-28].

A unified model describing the effects of antenna array design as wellas the characteristics of the propagation channel on the spatial diversity (or degreesof freedom) of wireless links was presented in [29]. The received signal model in [29]is given by( ) ( )( ) = , + ( )where ∈ C is the polarized vector describing the transmit signal, , ∈ R arethe polarized vector positions describing the transmit and receive arrays, respectively,and ∙,∙ ∈ C is the matrix describing the system response between transmit andreceive vector positions given by (, ) = (, ) ( , ) ( , ) where (∙,∙), (∙,∙) ∈ C are the transmit and receive array responses respectivelyand ( , ) ∈ C is the channel response matrix with entries being the complexgains between transmit direction and receive direction . In DIDO systems, userdevices may have single or multiple antennas. For the sake of simplicity, we assumesingle antenna receivers with ideal isotropic patterns and rewrite the system responsematrix as( ) ( ) ( ) , = , , where only the transmit antenna pattern , is considered.

From the Maxwell equations and the far-field term of the Greenfunction, the array response can be approximated as [29] ( , ) = ( − ) a( , )2λ dwith ϵ P , P is the space that defines the antenna array and wherea( , ) = exp(−j2π )with ( , )ϵ Ω × P. For unpolarized antennas, studying the array response is equivalentto study the integral kernel above. Hereafter, we show closed for expressions of theintegral kernels for different types of arrays.

Unpolarized Linear Arrays For unpolarized linear arrays of length L (normalized by thewavelength) and antenna elements oriented along the z-axis and centered at theorigin, the integral kernel is given by [29]( ) ( )a cos , = exp −j2π cos .

Expanding the above equation into a series of shifted dyads, we obtainthat the sinc function have resolution of 1/L and the dimension of the array-limitedand approximately wavevector-limited subspace (i.e., degrees of freedom) isD = L Ωwhere Ω = cos : Θ . We observe that for broadside arrays |Ω | = |Θ| whereas forendfire |Ω | ≈ |Θ| /2.

Unpolarized Spherical Arrays The integral kernel for a spherical array of radius R (normalized by thewavelength) is given by [29] a( , ) = exp −j2πR sin sin cos( − ) + cos cos .

Decomposing the above function with sum of spherical Besselfunctions of the first kind we obtain the resolution of spherical arrays is 1/( πR ) andthe degrees of freedom are given by| | | |D = Ω = πR Ω where A is the area of the spherical array and| | ) )Ω ⊂ 0, π × 0,2π .

Areas of Coherence in Wireless Channels The relation between the resolution of spherical arrays and their area Ais depicted in Figure 43. The sphere in the middle is the spherical array of area A.

The projection of the channel clusters on the unit sphere defines different scatteringregions of size proportional to the angular spread of the clusters. The area of size1/A within each cluster, which we call “area of coherence”, denotes the projection ofthe basis functions of the radiated field of the array and defines the resolution of thearray in the wavevector domain.

Comparing Figure 43 with Figure 44, we observe that the size of thearea of coherence decreases as the inverse of the size of the array. In fact, largerarrays can focus energy into smaller areas, yielding larger number of degrees offreedom D . Note that to total number of degrees of freedom depends also on theangular spread of the cluster, as shown in the definition above.

Figure 45 depicts another example where the array size covers evenlarger area than Figure 44, yielding additional degrees of freedom. In DIDOsystems, the array aperture can be approximated by the total area covered by allDIDO transmitters (assuming antennas are spaced fractions of wavelength apart).

Then Figure 45 shows that DIDO systems can achieve increasing numbers ofdegrees of freedom by distributing antennas in space, thereby reducing the size ofthe areas of coherence. Note that these figures are generated assuming idealspherical arrays. In practical scenarios, DIDO antennas spread random across wideareas and the resulting shape of the areas of coherence may not be as regular as inthe figures.

Figure 46 shows that, as the array size increases, more clusters areincluded within the wireless channel as radio waves are scatterered by increasingnumber of objects between DIDO transmitters. Hence, it is possible to excite anincreasing number of basis functions (that span the radiated field), yielding additionaldegrees of freedom, in agreement with the definition above.

The multi-user (MU) multiple antenna systems (MAS) described in thispatent application exploit the area of coherence of wireless channels to createmultiple simultaneous independent non-interfering data streams to different users.

For given channel conditions and user distribution, the basis functions of the radiatedfield are selected to create independent and simultaneous wireless links to differentusers in such a way that every user experiences interference-free links. As the MU-MAS is aware of the channel between every transmitter and every user, theprecoding transmission is adjusted based on that information to create separateareas of coherence to different users.

In one embodiment of the invention, the MU-MAS employs non-linearprecoding, such as dirty-paper coding (DPC) [30-31] or Tomlinson-Harashima (TH)[32-33] precoding. In another embodiment of the invention, the MU-MAS employsnon-linear precoding, such as block diagonalization (BD) as in our previous patentapplications [0003-0009] or zero-forcing beamforming (ZF-BF) [34].

To enable precoding, the MU-MAS requires knowledge of the channelstate information (CSI). The CSI is made available to the MU-MAS via a feedbackchannel or estimated over the uplink channel, assuming uplink/downlink channelreciprocity is possible in time division duplex (TDD) systems. One way to reduce theamount of feedback required for CSI, is to use limited feedback techniques [35-37].

In one embodiment, the MU-MAS uses limited feedback techniques to reduce theCSI overhead of the control channel. Codebook design is critical in limited feedbacktechniques. One embodiment defines the codebook from the basis functions thatspan the radiated field of the transmit array.

As the users move in space or the propagation environment changesover time due to mobile objects (such as people or cars), the areas of coherencechange their locations and shape. This is due to the well known Doppler effect inwireless communications. The MU-MAS described in this patent application adjuststhe precoding to adapt the areas of coherence constantly for every user as theenvironment changes due to Doppler effects. This adaptation of the areas ofcoherence is such to create simultaneous non-interfering channels to different users.

Another embodiment of the invention adaptively selects a subset ofantennas of the MU-MAS system to create areas of coherence of different sizes. Forexample, if the users are sparsely distributed in space (i.e., rural area or times of theday with low usage of wireless resources), only a small subset of antennas isselected and the size of the area of coherence are large relative to the array size asin Figure 43. Alternatively, in densely populated areas (i.e., urban areas or time ofthe day with peak usage of wireless services) more antennas are selected to createsmall areas of coherence for users in direct vicinity of each other.

In one embodiment of the invention, the MU-MAS is a DIDO system asdescribed in previous patent applications [0003-0009]. The DIDO system uses linearor non-linear precoding and/or limited feedback techniques to create area ofcoherence to different users.

Numerical Results We begin by computing the number of degrees of freedom inconventional multiple-input multiple-output (MIMO) systems as a function of the arraysize. We consider unpolarized linear arrays and two types of channel models: indooras in the IEEE 802.11n standard for WiFi systems and outdoor as in the 3GPP-LTEstandard for cellular systems. The indoor channel mode in [3] defines the number ofclusters in the range [2, 6] and angular spread in the range [15 , 40 ]. The outdoorchannel model for urban micro defines about 6 clusters and the angular spread atthe base station of about 20 .

Figure 47 shows the degrees of freedom of MIMO systems in practicalindoor and outdoor propagation scenarios. For example, considering linear arrayswith ten antennas spaced one wavelength apart, the maximum degrees of freedom(or number of spatial channels) available over the wireless link is limited to about 3for outdoor scenarios and 7 for indoor. Of course, indoor channels provide moredegrees of freedom due to the larger angular spread.

Next we compute the degrees of freedom in DIDO systems. Weconsider the case where the antennas distributed over 3D space, such as downtownurban scenarios where DIDO access points may be distributed on different floors ofadjacent building. As such, we model the DIDO transmit antennas (all connected toeach other via fiber or DSL backbone) as a spherical array. Also, we assume theclusters are uniformly distributed across the solid angle.

Figure 48 shows the degrees of freedom in DIDO systems as afunction of the array diameter. We observe that for a diameter equal to tenwavelengths, about 1000 degrees of freedom are available in the DIDO system. Intheory, it is possible to create up to 1000 non-interfering channels to the users. Theincreased spatial diversity due to distributed antennas in space is the key to themultiplexing gain provided by DIDO over conventional MIMO systems.

As a comparison, we show the degrees of freedom achievable insuburban environments with DIDO systems. We assume the clusters are distributedwithin the elevation angles [α, π − α], and define the solid angle for the clusters asΩ = 4π cos α. For example, in suburban scenarios with two-story buildings, theelevation angle of the scatterers can be α = 60 . In that case, the number of degreesof freedom as a function of the wavelength is shown in Figure 48.

IV. SYSTEM AND METHODS FOR PLANNED EVOLUTION ANDOBSOLESCENCE OF MULTIUSER SPECTRUM The growing demand for high-speed wireless services and theincreasing number of cellular telephone subscribers has produced a radicaltechnology revolution in the wireless industry over the past three decades from initialanalog voice services (AMPS [1-2]) to standards that support digital voice (GSM [3-4], IS-95 CDMA [5]), data traffic (EDGE [6], EV-DO [7]) and Internet browsing (WiFi[8-9], WiMAX [10-11], 3G [12-13], 4G [14-15]). This wireless technology growththroughout the years has been enabled by two major efforts:i) The federal communications commission (FCC) [16] has been allocating newspectrum to support new emerging standards. For example, in the first generationAMPS systems the number of channels allocated by the FCC grew from the initial 333in 1983 to 416 in the late 1980s to support the increasing number of cellular clients.

More recently, the commercialization of technologies like Wi-Fi, Bluetooth and ZigBeehas been possible with the use of the unlicensed ISM band allocated by the FCC backin 1985 [17].ii) The wireless industry has been producing new technologies that utilize thelimited available spectrum more efficiently to support higher data rate links andincreased numbers of subscribers. One big revolution in the wireless world was themigration from the analog AMPS systems to digital D-AMPS and GSM in the 1990s,that enabled much higher call volume for a given frequency band due to improvedspectral efficiency. Another radical shift was produced in the early 2000s by spatialprocessing techniques such as multiple-input multiple-output (MIMO), yielding 4ximprovement in data rate over previous wireless networks and adopted by differentstandards (i.e., IEEE 802.11n for Wi-Fi, IEEE 802.16 for WiMAX, 3GPP for 4G-LTE).

Despite efforts to provide solutions for high-speed wirelessconnectivity, the wireless industry is facing new challenges: to offer high-definition(HD) video streaming to satisfy the growing demand for services like gaming and toprovide wireless coverage everywhere (including rural areas, where building thewireline backbone is costly and impractical). Currently, the most advanced wirelessstandard systems (i.e., 4G-LTE) cannot provide data rate requirements and latencyconstraints to support HD streaming services, particularly when the network isoverloaded with a high volume of concurrent links. Once again, the main drawbackshave been the limited spectrum availability and lack of spectrally efficienttechnologies that can truly enhance data rate and provide complete coverage.

A new technology has emerged in recent years called distributed-inputdistributed-output (DIDO) [18-21] and described in our previous patent applications[0002-0009]. DIDO technology promises orders of magnitude increase in spectralefficiency, making HD wireless streaming services possible in overloaded networks.

At the same time, the US government has been addressing the issueof spectrum scarcity by launching a plan that will free 500MHz of spectrum over thenext 10 years. This plan was released on June 28 , 2010 with the goal of allowingnew emerging wireless technologies to operate in the new frequency bands andproviding high-speed wireless coverage in urban and rural areas [22]. As part of thisplan, on September 23 , 2010 the FCC opened up about 200MHz of the VHF andUHF spectrum for unlicensed use called “white spaces” [23]. One restriction tooperate in those frequency bands is that harmful interference must not be createdwith existing wireless microphone devices operating in the same band. As such, onJuly 22 , 2011 the IEEE 802.22 working group finalized the standard for a newwireless system employing cognitive radio technology (or spectrum sensing) with thekey feature of dynamically monitoring the spectrum and operating in the availablebands, thereby avoiding harmful interference with coexisting wireless devices [24].

Only recently has there been debates to allocate part of the white spaces to licenseduse and open it up to spectrum auction [25].

The coexistence of unlicensed devices within the same frequencybands and spectrum contention for unlicensed versus licensed use have been twomajor issues for FCC spectrum allocation plans throughout the years. For example,in white spaces, coexistence between wireless microphones and wirelesscommunications devices has been enabled via cognitive radio technology. Cognitiveradio, however, can provide only a fraction of the spectral efficiency of othertechnologies using spatial processing like DIDO. Similarly, the performance of Wi-Fisystems have been degrading significantly over the past decade due to increasingnumber of access points and the use of Bluetooth/ZigBee devices that operate in thesame unlicensed ISM band and generate uncontrolled interference. Oneshortcoming of the unlicensed spectrum is unregulated use of RF devices that willcontinue to pollute the spectrum for years to come. RF pollution also prevents theunlicensed spectrum from being used for future licensed operations, thereby limitingimportant market opportunities for wireless broadband commercial services andspectrum auctions.

We propose a new system and methods that allow dynamic allocationof the wireless spectrum to enable coexistence and evolution of different servicesand standards. One embodiment of our method dynamically assigns entitlements toRF transceivers to operate in certain parts of the spectrum and enablesobsolescence of the same RF devices to provide:i) Spectrum reconfigurability to enable new types of wireless operations (i.e.,licensed vs. unlicensed) and/or meet new RF power emission limits. This featureallows spectrum auctions whenever is necessary, without need to plan in advance foruse of licensed versus unlicensed spectrum. It also allows transmit power levels to beadjusted to meet new power emission levels enforced by the FCC.ii) Coexistence of different technologies operating in the same band (i.e., whitespaces and wireless microphones, WiFi and Bluetooth/ZigBee) such that the band canbe dynamically reallocated as new technologies are created, while avoidinginterference with existing technologies.iii) Seamless evolution of wireless infrastructure as systems migrate to moreadvanced technologies that can offer higher spectral efficiency, better coverage andimproved performance to support new types of services demanding higher QoS (i.e.,HD video streaming).

Hereafter, we describe a system and method for planned evolution andobsolescence of a multiuser spectrum. One embodiment of the system consists ofone or multiple centralized processors (CP) 4901-4904 and one or multipledistributed nodes (DN) 4911-4913 that communicate via wireline or wirelessconnections as depicted in Figure 49. For example, in the context of 4G-LTEnetworks [26], the centralized processor is the access core gateway (ACGW)connected to several Node B transceivers. In the context of Wi-Fi, the centralizedprocessor is the internet service provider (ISP) and the distributed nodes are Wi-Fiaccess points connected to the ISP via modems or direct connection to cable orDSL. In another embodiment of the invention, the system is a distributed-inputdistributed-output (DIDO) system [0002-0009] with one centralized processor (orBTS) and distributed nodes being the DIDO access points (or DIDO distributedantennas connected to the BTS via the BSN).

The DNs 4911-4913 communicate with the CPs 4901-4904. Theinformation exchanged from the DNs to the CP is used to dynamically adjust theconfiguration of the nodes to the evolving design of the network architecture. In oneembodiment, the DNs 4911-4913 share their identification number with the CP. TheCP store the identification numbers of all DNs connected through the network intolookup tables or shared database. Those lookup tables or database can be sharedwith other CPs and that information is synchronized such that all CPs have alwaysaccess to the most up to date information about all DNs on the network.

For example, the FCC may decide to allocate a certain portion of thespectrum to unlicensed use and the proposed system may be designed to operatewithin that spectrum. Due to scarcity of spectrum, the FCC may subsequently needto allocate part of that spectrum to licensed use for commercial carriers (i.e., AT&T,Verizon, or Sprint), defense, or public safety. In conventional wireless systems, thiscoexistence would not be possible, since existing wireless devices operating in theunlicensed band would create harmful interference to the licensed RF transceivers.

In our proposed system, the distributed nodes exchange control information with theCPs 4901-4903 to adapt their RF transmission to the evolving band plan. In oneembodiment, the DNs 4911-4913 were originally designed to operate over differentfrequency bands within the available spectrum. As the FCC allocates one or multipleportions of that spectrum to licensed operation, the CPs exchange controlinformation with the unlicensed DNs and reconfigure them to shut down thefrequency bands for licensed use, such that the unlicensed DNs do not interfere withthe licensed DNs. This scenario is depicted in Figure 50 where the unlicensednodes (e.g., 5002) are indicated with solid circles and the licensed nodes with emptycircles (e.g., 5001). In another embodiment, the whole spectrum can be allocated tothe new licensed service and the control information is used by the CPs to shut downall unlicensed DNs to avoid interference with the licensed DNs. This scenario isshown in Figure 51 where the obsolete unlicensed nodes are covered with a cross.

By way of another example, it may be necessary to restrict poweremissions for certain devices operating at given frequency band to meet the FCCexposure limits [27]. For instance, the wireless system may originally be designedfor fixed wireless links with the DNs 4911-4913 connected to outdoor rooftoptransceiver antennas. Subsequently, the same system may be updated to supportDNs with indoor portable antennas to offer better indoor coverage. The FCCexposure limits of portable devices are more restrictive than rooftop transmitters, dueto possibly closer proximity to the human body. In this case, the old DNs designedfor outdoor applications can be re-used for indoor applications as long as thetransmit power setting is adjusted. In one embodiment of the invention the DNs aredesigned with predefined sets of transmit power levels and the CPs 4901-4903 sendcontrol information to the DNs 4911-4913 to select new power levels as the systemis upgraded, thereby meeting the FCC exposure limits. In another embodiment, theDNs are manufactured with only one power emission setting and those DNsexceeding the new power emission levels are shut down remotely by the CP.

In one embodiment, the CPs 4901-4903 monitor periodically all DNs4911-4913 in the network to define their entitlement to operate as RF transceiversaccording to a certain standard. Those DNs that are not up to date can be marked asobsolete and removed from the network. For example, the DNs that operate withinthe current power limit and frequency band are kept active in the network, and all theothers are shut down. Note that the DN parameters controlled by the CP are notlimited to power emission and frequency band; it can be any parameter that definesthe wireless link between the DN and the client devices.

In another embodiment of the invention, the DNs 4911-4913 can bereconfigured to enable the coexistence of different standard systems within the samespectrum. For example, the power emission, frequency band or other configurationparameters of certain DNs operating in the context of WLAN can be adjusted toaccommodate the adoption of new DNs designed for WPAN applications, whileavoiding harmful interference.

As new wireless standards are developed to enhance data rate andcoverage in the wireless network, the DNs 4911-4913 can be updated to supportthose standards. In one embodiment, the DNs are software defined radios (SDR)equipped with programmable computational capability such as such as FPGA, DSP,CPU, GPU and/or GPGPU that run algorithms for baseband signal processing. If thestandard is upgraded, new baseband algorithms can be remotely uploaded from theCP to the DNs to reflect the new standard. For example, in one embodiment the firststandard is CDMA-based and subsequently it is replaced by OFDM technology tosupport different types of systems. Similarly, the sample rate, power and otherparameters can be updated remotely to the DNs. This SDR feature of the DNsallows for continuous upgrades of the network as new technologies are developed toimprove overall system performance.

In another embodiment, the system described herein is a cloudwireless system consisting of multiple CPs, distributed nodes and a networkinterconnecting the CPs to the DNs. Figure 52 shows one example of cloud wirelesssystem where the nodes identified with solid circles (e.g., 5203) communicate to CP5206, the nodes identified with empty circles communicate to CP 5205 and the CPs5205-5206 communicate between each other all through the network 5201. In oneembodiment of the invention, the cloud wireless system is a DIDO system and theDNs are connected to the CP and exchange information to reconfigure periodicallyor instantly system parameters, and dynamically adjust to the changing conditions ofthe wireless architecture. In the DIDO system, the CP is the DIDO BTS, thedistributed nodes are the DIDO distributed antennas, the network is the BSN andmultiple BTSs are interconnected with each other via the DIDO centralized processoras described in our previous patent applications [0002-0009].

All DNs 5202-5203 within the cloud wireless system can be grouped indifferent sets. These sets of DNs can simultaneously create non-interfering wirelesslinks to the multitude of client devices, while each set supporting a different multipleaccess techniques (e.g., TDMA, FDMA, CDMA, OFDMA and/or SDMA), differentmodulations (e.g., QAM, OFDM) and/or coding schemes (e.g., convolutional coding,LDPC, turbo codes). Similarly, every client may be served with different multipleaccess techniques and/or different modulation/coding schemes. Based on the activeclients in the system and the standard they adopt for their wireless links, the CPs5205-5206 dynamically select the subset of DNs that can support those standardsand that are within range of the client devices.

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[12] 3GPP, “UMTS”, http://www.3gpp.org/article/umts

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[14] 3GPP, “LTE”, http://www.3gpp.org/LTE

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[20] Wired, “Has OnLive’s Steve Perlman Discovered Holy Grail ofWireless?”, June 30, 2011http://www.wired.com/epicenter/2011/06/perlman-holy-grail-wireless/

[21] The Wall Street Journal “Silicon Valley Inventor’s Radical Rewriteof Wireless”, July 28, 2011http://blogs.wsj.com/digits/2011/07/28/silicon-valley-inventors-radical-rewrite-of-wireless/

[22] The White House, “Presidential Memorandum: Unleashing theWireless Broadband Revolution”, June 28, 2010http://www.whitehouse.gov/the-press-office/presidential-memorandum-unleashing-wireless-broadband-revolution

[23] FCC, “Open commission meeting”, Sept. 23 , 2010http://reboot.fcc.gov/open-meetings/2010/september

[24] IEEE 802.22, “IEEE 802.22 Working Group on Wireless RegionalArea Networks”, http://www.ieee802.org/22/

[25] “A bill”,112th congress, 1 session, July 12, 2011http://republicans.energycommerce.house.gov/Media/file/Hearings/Telecom/071511/DiscussionDraft.pdf

[26] H. Ekström, A. Furuskär, J. Karlsson, M. Meyer, S. Parkvall, J.

Torsner, and M. Wahlqvist “Technical Solutions for the 3G Long-Term Evolution”,IEEE Communications Magazine, pp.38-45, Mar. 2006

[27] FCC, “Evaluating compliance with FCC guidelines for humanexposure to radiofrequency electromagnetic fields,” OET Bulletin 65, Edition 97-01,Aug. 1997 Embodiments of the invention may include various steps as set forthabove. The steps may be embodied in machine-executable instructions which causea general-purpose or special-purpose processor to perform certain steps. Forexample, the various components within the Base Stations/APs and Client Devicesdescribed above may be implemented as software executed on a general purpose orspecial purpose processor. To avoid obscuring the pertinent aspects of theinvention, various well known personal computer components such as computermemory, hard drive, input devices, etc., have been left out of the figures.

Alternatively, in one embodiment, the various functional modulesillustrated herein and the associated steps may be performed by specific hardwarecomponents that contain hardwired logic for performing the steps, such as anapplication-specific integrated circuit (“ASIC”) or by any combination of programmedcomputer components and custom hardware components.

In one embodiment, certain modules such as the Coding, Modulationand Signal Processing Logic 903 described above may be implemented on aprogrammable digital signal processor (“DSP”) (or group of DSPs) such as a DSPusing a Texas Instruments’ TMS320x architecture (e.g., a TMS320C6000,TMS320C5000, . . . etc). The DSP in this embodiment may be embedded within anadd-on card to a personal computer such as, for example, a PCI card. Of course, avariety of different DSP architectures may be used while still complying with theunderlying principles of the invention.

Elements of the present invention may also be provided as a machine-readable medium for storing the machine-executable instructions. The machine-readable medium may include, but is not limited to, flash memory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards,propagation media or other type of machine-readable media suitable for storingelectronic instructions. For example, the present invention may be downloaded as acomputer program which may be transferred from a remote computer (e.g., a server)to a requesting computer (e.g., a client) by way of data signals embodied in a carrierwave or other propagation medium via a communication link (e.g., a modem ornetwork connection).

Throughout the foregoing description, for the purposes of explanation,numerous specific details were set forth in order to provide a thorough understandingof the present system and method. It will be apparent, however, to one skilled in theart that the system and method may be practiced without some of these specificdetails. Accordingly, the scope and spirit of the present invention should be judgedin terms of the claims which follow.

Moreover, throughout the foregoing description, numerous publicationswere cited to provide a more thorough understanding of the present invention. All ofthese cited references are incorporated into the present application by reference.

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Claims (4)

1. A method comprising: distributing a plurality of transceiver devices and connected antennas of a multi-user multi-antenna system (MU-MAS); simultaneously transmitting pre-coded radio signals from the plurality of distributed transceiver devices, the pre-coding causing deliberate radio frequency interference to create concurrent, non-interfering enclosed shapes in space of coherent wireless signals, adjusting the pre-coding to adapt a size of the enclosed shapes in space, and adapting the size of the enclosed shapes in space by selecting a subset of distributed transceiver devices.

2. A method comprising: distributing a plurality of transceiver devices and connected antennas of a multi-user multi-antenna system (MU-MAS); simultaneously transmitting pre-coded radio signals from the plurality of distributed transceiver devices, the pre-coding causing deliberate radio frequency interference to create concurrent, non-interfering enclosed shapes in space of coherent wireless signals, adjusting the pre-coding to adapt a size of the enclosed shapes in space, and changing the size of the enclosed shapes in space by adjusting spacing of the distributed transceiver devices.

3. A method comprising: distributing a plurality of transceiver devices and connected antennas of a multi-user multi-antenna system (MU-MAS); simultaneously transmitting pre-coded radio signals from the plurality of distributed transceiver devices, the pre-coding causing deliberate radio frequency interference to create concurrent, non-interfering enclosed shapes in space of coherent wireless signals, adjusting the pre-coding to adapt a size of the enclosed shapes in space, and changing the size of the enclosed shapes in space by adjusting elevations of the distributed transceiver devices.

4. A method comprising: distributing a plurality of transceiver devices and connected antennas of a multi-user multi-antenna system (MU-MAS); simultaneously transmitting pre-coded radio signals from the plurality of distributed transceiver devices, the pre-coding causing deliberate radio frequency interference to create concurrent, non-interfering enclosed shapes in space of coherent wireless signals, adjusting the pre-coding to adapt a size of the enclosed shapes in space, and changing the size of the enclosed shapes in space by adjusting angular diversity of the distributed transceiver devices.