Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a channel estimation method, a system, equipment, a medium and a terminal based on clustering.
The invention is realized in such a way that a channel estimation method based on clustering comprises the following steps:
S1, converting a target signal into a beam domain;
s2, clustering the obtained beam domain signals;
S3, screening the obtained clusters among the clusters;
S4, carrying out intra-cluster screening on the screened clusters;
s5, carrying out reverse reconstruction on the channel according to the wave beams obtained by screening.
In the step S1, the target signal is a time-frequency domain signal or a time-frequency domain channel signal after preliminary estimation, wherein the channel after preliminary estimation is maximum ratio combination MRC (Maximal Ratio Combining), least mean square LS (Least Squares) channel estimation and linear least mean square error LMMSE (Least Minimum Mean Square Error) channel estimation;
Further, the obtained beam domain signals are clustered in S2, and parameters referred to by clustering include, but are not limited to, beam delay, beam arrival angle and beam energy. The parameters used for a particular cluster may be one or more combinations of the listed reference parameters, depending on the circumstances.
Further, the obtained beam clusters are screened, and clusters with cluster energy smaller than noise energy are removed.
Further, criteria for inter-cluster screening include:
① Clusters that should be reserved are selected based on a threshold. The clusters are arranged in energy from large to small, clusters are selected for which the cluster energy sum is greater than a defined threshold, such as clusters are selected for which the cluster sum energy ratio is greater than 85% of the total energy.
② Clusters that should be reserved are selected based on the segmentation threshold. And dividing the threshold according to the received channel signal or the energy of the channel signal after preliminary estimation. And selecting the cluster according to a preset threshold for the corresponding segment.
③ And selecting the cluster according to the adaptive threshold. As an example, the adaptive threshold may be the total number of beams divided by the total number of clusters, multiplied by the noise energy.
④ Clusters are selected based on artificial intelligence. As an embodiment, the capability of the artificial intelligent neural network to identify the features can be utilized to learn the mapping relation between the real clusters and the received channel signals or the preliminary estimated channels, and the clusters are screened through the mapping relation.
⑤ And selecting the cluster according to the time delay. As an example, the impact of interference can be filtered by means of delays to remove clusters that are out of the designed communication range and are too energetic.
⑥ Clusters are selected according to beam direction. As an embodiment, the influence of the interference caused by the beam direction can be screened to remove clusters whose incoming wave positions are inconsistent with the beam positions of the base station in communication.
⑦ And selecting the cluster according to the time delay and azimuth combination. As one example, clusters are removed where the delay is outside the designed communication range, the energy is too high, and the incoming wave orientation is not consistent with the communication base station beam orientation.
And selecting the selected beam clusters in the clusters, and selecting peak spot beams of each cluster according to the energy of each beam in the clusters.
Further, the selected beam position is reserved, beams at other positions are set to be zero, and the filtered beam domain channel is inversely transformed to a time-frequency domain, so that a clustering enhanced channel estimation result can be obtained.
Another object of the present invention is to provide a clustering-based channel estimation system implementing the clustering-based channel estimation method, comprising:
a target signal conversion module for converting a target signal to a beam domain;
The clustering processing module is used for carrying out clustering processing on the obtained beam domain signals;
The inter-cluster screening module is used for carrying out inter-cluster screening on the obtained clusters;
the cluster screening module is used for carrying out cluster screening on the screened clusters;
And the reverse reconstruction module is used for carrying out reverse reconstruction on the channel according to the wave beams obtained by screening.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the method and steps of cluster-based channel estimation.
It is a further object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the method and steps of cluster-based channel estimation.
Another object of the present invention is to provide an information data processing terminal including the cluster-based channel estimation system.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
the first, the invention uses the cluster as the cut-in point based on the clustered channel estimation, carries out the space wave beam domain filtering to the channel, and can effectively filter the influence of the false peak point in practice, thereby enhancing the channel estimation and avoiding the performance loss.
The technical scheme of the invention mainly solves the following technical problems in the prior art, and achieves remarkable technical progress:
1. Technical problem in the prior art
1) The channel estimation precision is low, in a multipath propagation environment, the traditional beam domain channel estimation method is often influenced by signal noise and interference, and false peak points are erroneously selected to reconstruct a channel, so that the channel estimation precision is not high. Existing methods often have difficulty in effectively distinguishing the primary path from the secondary path, and are prone to estimation errors, thereby affecting the overall performance of the communication system.
2) The robustness in the complex environment is insufficient, namely, in the complex wireless communication environment, the signal propagation path is complex and changeable, and the prior art cannot always maintain stable estimation performance under the conditions of multipath interference, signal fading and the like, so that the system reliability is insufficient.
3) The calculation complexity is high, the calculation amount is large, the processing efficiency is low, and the real-time processing requirement in the practical application is difficult to meet when the traditional channel estimation method is used for processing a large-scale antenna array or a complex scene. The high computational complexity not only increases the hardware requirement of the system, but also prolongs the processing time, and influences the real-time performance of the system.
2. Significant technical advances of the invention
1) The invention can effectively distinguish the dominant path and noise interference and improve the accuracy of channel estimation by converting the target signal into the wave beam domain and adopting clustering treatment and multi-layer screening. Especially, through inter-cluster screening and intra-cluster screening, the system can accurately extract useful signals in a multipath environment, and estimation errors are reduced.
2) The system robustness is enhanced, namely, the system robustness and stability are obviously improved by utilizing the clustering processing technology, and the system can be better adapted to complex scenes such as multipath propagation, signal fading and the like in a complex wireless environment. The robustness can ensure that the system can still keep higher channel estimation performance when facing different propagation environments, thereby improving the overall reliability of the communication system.
3) The method and the device reduce the calculation complexity, namely the channel estimation problem is decomposed into a plurality of small-scale problems through a clustering technology, and the calculation complexity is effectively reduced. The in-cluster screening reduces unnecessary calculation amount, so that the system can obviously improve the processing efficiency on the premise of ensuring the estimation accuracy, and meets the real-time processing requirements of a large-scale antenna array and a complex scene.
In summary, the invention has made remarkable technical progress in solving the problems of accuracy, robustness and computational complexity of channel estimation, and provides a more efficient and reliable channel estimation method for modern wireless communication systems.
The expected benefits and commercial values after the technical scheme is converted are that the clustering-based channel estimation can well filter the influence caused by the false peak point, and the accuracy of the channel estimation of the wireless communication system is improved, especially in a low signal-to-noise ratio scene. The source of the "false" peak point is usually three types, namely random superposition of electromagnetic wave reflection, refraction and diffraction, random noise and random interference. The 3GPP commercial wireless communication system generally adopts a paid frequency band, and the influence of a false peak point caused by random interference is smaller than the influence of random superposition and random noise mainly caused by multipath effect. For 3GPP commercial wireless communications, achieving better channel estimation performance at low signal-to-noise ratios means an expansion of base station coverage without too dense deployment of base stations. Therefore, the clustering-based channel estimation technology provided by the invention can greatly save the cost for realizing the 3GPP wireless communication system. For wireless communication systems employing common frequency bands, such as WiFi and bluetooth, in addition to multipath effects and random noise, the time facets are subject to "spurious" peak effects from random interference. The clustering-based channel estimation provided by the invention can greatly improve the performance of the wireless communication system adopting the public frequency band, thereby having extremely high commercial value. In addition to wireless communication systems, wired communication systems are also subject to random crosstalk, and the clustering-based channel estimation provided by the invention has high commercial value potential in the wired communication systems.
The technical scheme fills the technical blank in the domestic and foreign industries that the existing beam domain channel estimation is to directly select beams, and the influence caused by false peaks formed by random superposition, random noise, random interference and the like caused by reflection, refraction and diffraction of electromagnetic waves in an actual system is not considered. Once a "false" peak appears, the existing beam domain channel estimation often treats it as a real beam, and performs reverse reconstruction on the channel according to the beam position where it is located, resulting in a loss of channel estimation performance. The clustering-based channel estimation provided by the invention effectively avoids the influence of false peak points while inheriting the channel estimation advantages of the existing beam domain, thus belonging to the initiative at home and abroad and filling the blank of the channel estimation technology.
The technical scheme of the invention solves the technical problems that people are always desirous of solving but are not successful all the time:
wireless communication systems employing a common frequency band, such as WiFi, cannot always be free from the influence caused by co-frequency random interference, so that the performance of these wireless communication systems faces a huge uncertainty, and thus it is difficult to deploy to industrial scenes with high requirements on certainty and stability. The clustering-based channel estimation provided by the invention can well filter the influence of random interference, so that the performance of a wireless communication system adopting a public frequency band becomes more definite and stable, thereby bringing a lower-cost solution to various commercial scenes such as industrial scenes.
The technical scheme of the invention overcomes the technical prejudice that people have the impressive impression of uncertain system performance on the wireless communication system adopting the public frequency band for a long time because of the existence of random interference, and the method is rarely used in scenes with higher certainty requirements, such as industrial production. The clustering-based channel estimation provided by the invention well solves the problem of the influence of false peak points caused by random interference, provides a solution for the use of a public frequency band wireless communication system in a scene with high certainty requirement, and overcomes the conventional impressing impression.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
For a mimo MIMO (Multiple Input and Multiple Output) wireless communication system equipped with multiple antenna units, assuming that the number of antenna units equipped at the base station side is M, the number of antenna units equipped at the user equipment side is N, and the number of transmission streams is S (s.ltoreq.n), the uplink channel matrix may be denoted as H e CMXS, where C represents the complex domain. In the training phase, the pre-designed reference signal xt∈CSX1 is sent by the user terminal at time t, and the observation signal yt received by the base station is expressed as
yt＝Htxt+n (1)
Where n ε CMX1 represents noise. Since the mobile communication device at the user side is powered by a battery, the power consumption and the computing power that can be provided by the mobile communication device are limited, and in practice, n=s=1 is usually present. Since the reference signal xt is known, then the least squares LS (Least Squares) channel estimate can be expressed as
Where HLS is a least squares estimate of the original channel signal Ht. The reference signal xt generally satisfies the orthogonality,Also frequently transposed with its conjugateInstead of it. The least squares channel estimation is widely used in industry because it is simple to implement. However, the disadvantage is also apparent that noise is greatly amplified when the channel condition is bad, resulting in a great decrease in channel estimation performance, since the noise term is not considered. Thus, linear least mean square error LMMSE (Least Minimum Mean Square Error) channel estimates are often used to improve the least squares LS channel estimates,
Where RHH＝E{HHH is the autocorrelation matrix of the channel signal, β is a constant determined by the transmitted signal constellation point (e.g., β=17/9 for 16-QAM), and SNR represents the signal-to-noise ratio. LMMSE-based methods can improve channel estimation performance, but their implementation is very complex. In particular it assumes that the channel autocorrelation matrix RHH is known, which is not true in practice. The RHH value is typically approximated by long-term collection and statistics of the LS estimated channel. This makes the acquisition of the channel statistics RHH required by LMMSE very difficult and inefficient in practical applications. To reduce the computational and implementation complexity of LMMSE channel estimation, researchers often exploit the time domain sparsity of wireless signals to approximately implement LMMSE channel estimation. For example, only the first L (1 < L < N) elements of g are considered, and in the cross correlation matrix Rgg, only the first L elements are reserved, and other elements are set to zero, so that the computational complexity is reduced. The time domain sparsity of the signal is also reflected in a eigenvalue space, SVD singular value decomposition is carried out on Rhh to obtain Rhh＝UAUH, and only eigenvalue vector with the rank of p is reserved, so that low-rank channel estimation is obtained
Where Δp is a diagonal matrix, the first p diagonal elements δk＝λk/(λk+β/SNR)(λk are eigenvalues of Rhh), the elements at other positions are zero. Low rank channel estimation can approach the theoretical optimal performance, but it still requires singular value decomposition operations with high computational complexity. On the other hand, both LMMSE and low rank channel estimation are based on the channel statistics Rhh being known. In practice, the acquisition of the channel statistics Rhh is very difficult and inefficient, and a sufficiently accurate approximation of Rhh needs to be obtained by means of long-term channel statistics, so that the implementation complexity and cost of the LMMSE and its low-rank channel estimation type algorithm are still high.
With further mining of signal sparsity, the orthogonal matching pursuit OMP (Orthogonal Matching Pursuit) and the base pursuit BP (Basis Pursuit) are free of dependence on channel statistics. Thus, the conventional channel estimation problem turns to sparse recovery and reconstruction problems. The conventional time domain channel estimation only collects the energy delay characteristic PDP (Power Delay Profile) of the channel, estimates the delay corresponding to the multipath peak point from the obtained PDP, and reconstructs the channel according to the position corresponding to the main energy delay. As the number of base station antennas increases, array signal processing has come to the fore. Taking a linear antenna array ULA (Uniform LINEAR ARRAY) equipped with M antenna elements as an example, the channel model between the linear antenna array ULA and a single antenna user can be expressed as
Where a0u(θ0) is a Line-of-sight (LOS) component, a0 is a path gain, the component corresponding to 1.ltoreq.p.ltoreq.P is a non-Line-of-sight (NLOS) path component, P is the total number of multipaths, θp is the angle of arrival AoA (Angle of Arrive) of the path P, u (θp) is commonly referred to as the Steering Vector of the antenna array, the elements of which can be expressed asM e [ q- (M-1)/2, q=0, 1, ], M-1], λ is the wavelength, d is the antenna spacing, typically satisfying d=λ/2. In a typical 3GPP environment, the angular spread ratio is generally limited between 2 ° -5 °, similar to time domain sparsity, wireless signals also exhibit spatial sparsity. Thus, the channel estimation problem turns into spatial orientation estimation of the main path. Estimation of the angle of arrival is a classical problem for array signal processing, such as MUSIC and ESPRIT algorithms. The classical algorithms of the array signal processing are mainly designed by radar perception initially, have pre-designed training sequences based on the assumption of blind estimation unlike a commercial wireless communication system, and have high complexity and are difficult to be suitable for the commercial wireless communication system. Similarly, researchers have proposed a series of spatial low-rank channel estimation algorithms, basically thinking that the received signals are transformed into the beam domain by means of fast fourier transform, the beam direction containing main energy is found, the beams in other directions are zeroed, and the zeroed channel is transformed back by inverse fourier transform, so that the fast estimation and reconstruction of the wireless channel are realized. The beam domain low-rank channel estimation utilizes the spatial sparsity of wireless signals to perform dimension reduction processing on the originally huge channel, reduces the demand of a large-scale antenna array system on radio frequency resources, and simultaneously effectively reduces the data pressure of the forward transmission (Fronthaul) between the edge antenna radio frequency end and the digital end.
The existing low-rank channel estimation of the beam domain all involves a key operation of selecting the peak point of the beam energy. The selection criteria is that the beams are generally arranged from large to small in terms of energy by means of a card threshold, and all beams whose sum of the beam energies is greater than a certain threshold, such as 85%, are sequentially selected. Then, the other beams are zeroed, and the channel is reversely reconstructed through the selected peak point. If the peak point is selected incorrectly, the accuracy of the channel estimation will be greatly reduced. Taking an indoor wireless environment as an example, scattering bodies such as various furniture, production equipment and materials are placed in a limited space, and electromagnetic waves are reflected, refracted, diffracted and the like by a plurality of scattering bodies, so that the possibility of forward superposition exists, a peak value is formed at a receiving end, and the peak value is a false peak value point for channel estimation, so that serious channel estimation distortion can be caused. Furthermore, a wireless communication system represented by WiFi adopts a common frequency band, which is easily interfered by other electronic devices operating on the common frequency band, and the interference appears as burst random interference, which also causes a "false" peak point, thereby affecting the quality of channel estimation. In addition, random noise, which is ubiquitous in communication systems, can also be a source of "false" peak points.
As shown in fig. 1, a channel estimation method based on clustering includes:
S1, converting a target signal into a beam domain;
s2, clustering the obtained beam domain signals;
S3, screening the obtained clusters among the clusters;
S4, carrying out intra-cluster screening on the screened clusters;
s5, carrying out reverse reconstruction on the channel according to the wave beams obtained by screening.
Step S1:
First, the target signal is transformed into the beam domain. The beam domain is the angular resolution of the spatial signal to form beams in multiple directions such that the signal energy is concentrated in a few directions. This step obtains a beam domain signal by transforming the received time domain signal or frequency domain signal through a beam forming matrix. This transformation helps to highlight the main propagation path of the signal, simplifying subsequent processing steps.
Step S2:
Then, the obtained beam domain signals are clustered. In the beam domain, signal energy is typically concentrated on a few beams that represent the main propagation path of the signal. The purpose of the clustering process is to cluster these high energy beams together to form clusters. Each cluster represents a primary propagation path or reflection path. The clustering process may be implemented by setting an energy threshold or clustering algorithm to assign beams with similar directions or energy levels into the same cluster.
Step S3:
Then, the resulting clusters are subjected to inter-cluster screening. The purpose of the inter-cluster screening is to screen out the clusters that most contribute to the channel estimation among all clusters. The clusters with the largest channel gain are screened out by analyzing the energy, the position or other statistical characteristics of each cluster, and clusters with smaller contribution to channel estimation are eliminated. This step can significantly reduce computational complexity while preserving the most important signal components in the channel estimation.
Steps S4 and S5:
And finally, carrying out intra-cluster screening on the screened clusters, and carrying out reverse reconstruction on the channel according to the wave beams obtained by screening. Intra-cluster screening further refines the beams within each cluster, rejecting those that may be affected by noise or multipath effects. The clearest signal path in each cluster can be extracted through intra-cluster screening, and errors in channel estimation are reduced. And then, reversely reconstructing the channel by utilizing the screened wave beam information, and recovering the time domain or frequency domain characteristics of the channel, thereby completing the whole channel estimation process. By the clustering-based processing method, high-precision channel estimation results can be provided while complexity is reduced.
As shown in fig. 2, the cluster-based channel estimation system includes:
a target signal conversion module for converting a target signal to a beam domain;
The clustering processing module is used for carrying out clustering processing on the obtained beam domain signals;
The inter-cluster screening module is used for carrying out inter-cluster screening on the obtained clusters;
the cluster screening module is used for carrying out cluster screening on the screened clusters;
And the reverse reconstruction module is used for carrying out reverse reconstruction on the channel according to the wave beams obtained by screening.
The clustering-based channel estimation method mainly completes the processing and estimation of signals through a series of modules. First, in the target signal conversion module, the system converts the target signal into the beam domain after receiving it. This step is achieved by performing a specific mathematical transformation (e.g., fourier transform or discrete cosine transform) on the signal, in order to transform the signal from the time domain or frequency domain into the beam domain, so that the channel matrix is sparse, and the direction information in the signal is extracted more effectively, thereby improving the accuracy of the subsequent processing.
Next, the clustering processing module performs clustering processing on the signals converted into the beam domain. The purpose of the clustering process is to cluster beams with similar characteristics (e.g., spatial direction and channel conditions) together to form clusters. Each cluster represents a set of close propagation paths in the signal space. Through the clustering process, the system can effectively reduce noise interference and improve the accuracy of channel estimation.
The system then proceeds to the operation of the inter-cluster screening module and the intra-cluster screening module. The inter-cluster screening module screens out clusters that are most representative in the global scope, typically corresponding to the dominant propagation path of the signal, by analyzing the characteristics of the clustered signals. The screened clusters enter an intra-cluster screening module, and further fine screening is carried out on the signals in each cluster, so that noise and other interference signals are removed, and signal components which are most beneficial to channel estimation are reserved.
Finally, the system uses the filtered beam signals for the reverse reconstruction of the channel through a reverse reconstruction module. The inverse reconstruction is a reconstruction of the processed beam signal back into a time-domain or frequency-domain representation of the channel. By reconstructing these filtered beam signals, the system can accurately estimate the channel characteristics of the target signal. The clustering-based channel estimation method can remarkably improve the accuracy and reliability of channel estimation, and is particularly excellent in complex multipath propagation environments.
In order to further improve the robustness and the accuracy of channel estimation, the invention provides a clustering-based high-accuracy channel estimation enhancement scheme. Specifically, by examining the time delay, the arrival angle, the amplitude and the like of each point of a multipath channel, the invention clusters each point, finds the cluster with the largest energy in each iteration, and then obtains the peak point of each cluster, so that the false peak point can be effectively filtered, thereby improving the accuracy and the robustness of channel estimation, and the principle of the invention is shown in figure 3.
The implementation steps of the invention are as follows:
And step 1, beam domain transformation. And transforming the received time-frequency domain signal yt of the channel into a beam domain as shown in formula (1) or a time-frequency domain channel signal after preliminary estimation. Further, the preliminary estimated channel may be, but is not limited to:
① Maximum ratio combining MRC (Maximal Ratio Combining).
② Least mean square LS (Least Squares) channel estimation.
③ Linear minimum mean square error LMMSE (Least Minimum Mean Square Error) channel estimation.
And 2, beam clustering. The beam domain channels are clustered, and parameters referenced by the clustering include, but are not limited to, beam delay, beam angle of arrival, and beam energy. The parameters used for a particular cluster may be one or more combinations of the listed reference parameters, depending on the circumstances.
And 3, screening among clusters. And screening the obtained beam clusters, and removing clusters with cluster energy smaller than noise energy.
Further, the remaining beam clusters are subjected to secondary screening, wherein the screening criteria include, but are not limited to:
① Clusters that should be reserved are selected based on a threshold. The clusters are arranged from large to small, and clusters with the cluster energy summation larger than a defined threshold are sequentially selected, for example, clusters with the cluster summation energy ratio larger than 85% of the total energy are selected.
② Clusters that should be reserved are selected based on the segmentation threshold. And dividing the threshold according to the received channel signal or the energy of the channel signal after preliminary estimation. And selecting the cluster according to a preset threshold for the corresponding segment.
③ And selecting the cluster according to the adaptive threshold. As an example, the adaptive threshold may be the total beam energy divided by the total cluster number, multiplied by the noise energy.
④ Clusters are selected based on artificial intelligence. As an embodiment, the capability of the artificial intelligent neural network to identify the features can be utilized to learn the mapping relation between the real clusters and the received channel signals or the preliminary estimated channels, and the clusters are screened through the mapping relation.
⑤ And selecting the cluster according to the time delay. As an example, the impact of interference can be filtered by means of delays to remove clusters that are out of the designed communication range and are too energetic.
⑥ Clusters are selected according to beam direction. As an embodiment, the influence of the interference caused by the beam direction can be screened to remove clusters whose incoming wave positions are inconsistent with the beam positions of the base station in communication.
⑦ And selecting the cluster according to the time delay and azimuth combination. As one example, clusters are removed where the delay is outside the designed communication range, the energy is too high, and the incoming wave orientation is not consistent with the communication base station beam orientation.
And 4, screening in clusters. And selecting the selected beam clusters in the clusters, and selecting peak spot beams of each cluster according to the energy of each beam in the clusters.
And 5, channel reconstruction. And reserving the selected beam positions, setting the beams at other positions to zero, and inversely transforming the screened beam domain channels to a time-frequency domain to obtain a clustering enhanced channel estimation result.
The clustering-based channel estimation uses clusters as access points, and spatial beam domain filtering is carried out on the channels, so that the influence of false peak points in practice can be effectively filtered, the channel estimation is enhanced, and the performance loss is avoided.
The invention establishes a channel model for simulation according to 3GPP TR 38.901, the frequency band used is 5GHz, the antenna array is a uniform linear array ULA (Unitary LINEAR ARRAY), the number of antenna units is 64, and the channel scene is UMi (Urban Micro). As shown in fig. 4 and 5, the cluster-based selection of channel estimates (labeled ClusterSel in the figures) achieves more robust performance than the conventional threshold-based selection of beam-domain channel estimates (labeled BmSelThrd in the figures). Especially in low signal-to-noise scenarios, random fluctuations in noise are more likely to cause "false" peaks.
The simulations of fig. 4 to 5 only simulate the effects of urban multipath and noise on channel estimation, without adding the effects of interference. In practical systems, random interference is a significant source of "false" peaks.
It is worth noting that the invention transforms the time-frequency domain channel into the wave beam domain, so as to obtain a sparse channel, extract the direction information of the signal more accurately, and improve the accuracy of channel estimation. This beam domain transformation is not necessary, and the clustering-based channel estimation proposed by the present invention can also be directly applied to time-frequency domain channel estimation.
An application embodiment of the present invention provides a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of a cluster-based channel estimation method.
An application embodiment of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of a clustering-based channel estimation method.
The application embodiment of the invention provides an information data processing terminal, which comprises a channel estimation system based on clustering.
The false peak point is generally generated by random superposition of electromagnetic wave multipath effects, random noise and random interference, and an abnormal beam cluster formed by the false peak point is greatly different from a real beam cluster in aspects of beam delay, beam direction, beam energy and the like, so that the clustering-based channel estimation method can well distinguish the real cluster from the abnormal beam cluster by clustering the channel signal according to the beam delay, the beam direction, the beam energy and the like. As shown in fig. 4 and fig. 5, the present invention provides a clustering-based channel estimation and a threshold-based conventional beam domain channel estimation, which are applied to the received channel signals, respectively, and the clustering-based channel estimation achieves more robust performance, especially in a low signal-to-noise ratio scenario.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portions may be implemented using dedicated logic and the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or dedicated design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.