技术领域technical field
本发明涉及无线通信技术领域，特别涉及一种异构网络中的资源分配方法。The invention relates to the technical field of wireless communication, in particular to a resource allocation method in a heterogeneous network.
背景技术Background technique
移动通信技术经历了第一代模拟移动通信技术、第二代数字的以语音为主的窄带移动通信技术、第三代以高速互联网业务和多媒体业务为目的的宽带移动通信技术后，以LTE/LTE-A为代表的第四代宽带接入和分布网络系统已经投入商用。然而，对比LTE/LTE-A系统与3G系统，LTE/LTE-A系统的载波频率利用率虽有所升高，但其路径损耗相比3G系统也增大。因此，LTE/LTE-A系统不能很好的覆盖室内。有研究表明，近90％的数据业务和60％的语音业务是在室内发生的，所以LTE/LTE-A系统的室内覆盖问题亟需解决。Mobile communication technology has experienced the first generation of analog mobile communication technology, the second generation of digital narrowband mobile communication technology mainly based on voice, and the third generation of broadband mobile communication technology for high-speed Internet services and multimedia services. The fourth-generation broadband access and distribution network system represented by LTE-A has been put into commercial use. However, comparing the LTE/LTE-A system with the 3G system, although the carrier frequency utilization rate of the LTE/LTE-A system has increased, its path loss has also increased compared with the 3G system. Therefore, the LTE/LTE-A system cannot cover indoors well. Studies have shown that nearly 90% of data services and 60% of voice services occur indoors, so the indoor coverage problem of the LTE/LTE-A system needs to be resolved urgently.
毫微微小区(Femtocell)技术作为室内无线通信最有前景的技术之一，近年来得到了广泛的研究。Femtocell就是短距离、低功率、低成本的家庭基站所覆盖的小区，可以通过数字用户线(Digital Subscriber Line,DSL)或光纤与宏小区通信，并且与宏小区共享频带资源，不仅可以为用户提供更好的室内体验，还能够卸载宏小区网络流量，以及增加网络覆盖范围。然而，Femtocell在提升系统容量的同时也带来了新的问题，相比于传统的宏蜂窝网络，宏基站(Macro Base Station,MBS)和Femtocell Base Station(FBS)共存的异构网络中的干扰环境更加复杂。异构网络中不但存在原有的MBS间的同层干扰，又加入了MBS与FBS间的跨层干扰和FBS间的同层干扰。随着FBS密度的提高，这些干扰会变得更加严重。Femtocell technology, as one of the most promising technologies for indoor wireless communication, has been extensively studied in recent years. Femtocell is a cell covered by a short-distance, low-power, and low-cost home base station. It can communicate with the macro cell through a digital subscriber line (Digital Subscriber Line, DSL) or optical fiber, and share frequency band resources with the macro cell. It can not only provide users with A better indoor experience can also offload macro cell network traffic and increase network coverage. However, Femtocell also brings new problems while improving the system capacity. Compared with the traditional macro cellular network, the interference in the heterogeneous network where the macro base station (Macro Base Station, MBS) and Femtocell Base Station (FBS) coexist The environment is more complex. In the heterogeneous network, not only the original same-layer interference between MBSs exists, but also cross-layer interference between MBSs and FBSs and same-layer interference between FBSs are added. These disturbances become more severe as the FBS density increases.
针对以上存在的问题，目前相关文献已经提出了一些用于减小跨层干扰和同层干扰的方法，其中有学者为双层网络研究了一种干扰管理方案和资源分配策略，在降低了MBS受到的干扰前提下，又对毫微微用户(Femtocell User Equipment，FUE)进行了功率分配优化。还有学者提出了在保证FUE服务质量(Quality of Service，QOS)前提下的一种子信道和功率联合优化分配算法，并设置了干扰阈值来保护宏用户。但是，这些方法都没有考虑到在跨层干扰中是否会对宏用户(Macro User Equipment,MUE)的正常传输产生影响。有人提出了一种旨在最小化Femtocell之间的同层干扰的资源分配方案，确保FUE的QOS，但没有考虑到MUE的重要性。有学者在分组的基础上结合接入控制机制进行联合资源分配，以保证用户的QoS，但忽略了公平性的问题。In view of the above existing problems, some relevant literatures have proposed some methods for reducing cross-layer interference and same-layer interference. Among them, some scholars have studied an interference management scheme and resource allocation strategy for a two-layer network, which can reduce the MBS Under the premise of receiving interference, power allocation optimization is performed on Femtocell User Equipment (FUE). Some scholars have proposed a sub-channel and power joint optimal allocation algorithm under the premise of ensuring FUE Quality of Service (QOS), and set an interference threshold to protect macro users. However, these methods do not consider whether the normal transmission of a macro user (Macro User Equipment, MUE) will be affected during the cross-layer interference. Someone proposed a resource allocation scheme aimed at minimizing the same-layer interference between Femtocells to ensure the QOS of the FUE, but the importance of the MUE was not considered. Some scholars combine the access control mechanism on the basis of grouping for joint resource allocation to ensure the user's QoS, but ignore the issue of fairness.
发明人发现，随着FBS部署密度的增加，集中式协作的资源分配方案的计算复杂度随之提升，在FBS密集分布的场景中将很难实现，但是，若对FBS进行有效的分组，则可以很好地解决这个问题。The inventors found that with the increase of FBS deployment density, the computational complexity of the centralized collaborative resource allocation scheme will increase, and it will be difficult to implement in a densely distributed FBS scenario. However, if the FBS is effectively grouped, then can solve this problem very well.
发明内容Contents of the invention
针对以上现有技术的不足，本发明讨论了Macrocell-Femtocell网络中的资源分配问题，提出一种基于Femtocell分组的资源分配算法，可以有效抑制宏小区(Macrocell)用户层和Femtocell用户层之间的跨层干扰和同层干扰，有效地提升网络频谱效率。For the deficiencies in the prior art above, the present invention discusses the resource allocation problem in the Macrocell-Femtocell network, proposes a kind of resource allocation algorithm based on Femtocell grouping, can effectively suppress macrocell (Macrocell) user layer and between Femtocell user layer Cross-layer interference and same-layer interference can effectively improve network spectrum efficiency.
本发明的一种异构网络中的资源分配方法，包括以下步骤：A resource allocation method in a heterogeneous network of the present invention includes the following steps:
采用差额算法对宏用户MUEs执行子信道分配；Perform subchannel allocation for macro-user MUEs using a margin algorithm;
采用注水算法为每个MUEs子信道分配传输功率；Use the water filling algorithm to allocate transmission power for each MUEs sub-channel;
采用蚁群算法对毫微微小区Femtocell进行分组；Ant colony algorithm is used to group femtocells;
采用启发式算法对毫微微用户FUEs进行信道分配；Use a heuristic algorithm to allocate channels to femto user FUEs;
对FUEs进行功率分配。Power allocation to FUEs.
优选地，所述采用差额算法对MUEs执行子信道分配包括：Preferably, performing subchannel allocation on MUEs using a difference algorithm includes:
构建第一次迭代效益矩阵以为目标函数分配MUEs子信道，其中，为宏基站在子信道k上的发射功率，ξm.k∈{0,1}，表示子信道k的分配给宏用户的状态；是MUEm在子信道k上的信干噪比，M为宏用户总数，K为子信道总数，并且，若K<M，则添加M-K个虚拟MUEs，构建M×M阶方阵，若K>M，则添加K-M个虚拟子信道，构建K×K阶方阵。Construct the benefit matrix for the first iteration by Assign MUEs subchannels for the objective function, where, is the transmit power of the macro base station on sub-channel k, ξmk ∈ {0,1}, indicating the status of sub-channel k allocated to macro users; is the SINR of MUEm on sub-channel k, M is the total number of macro users, K is the total number of sub-channels, and, if K<M, add MK virtual MUEs to construct an M×M order square matrix, if K> M, then add KM virtual sub-channels to construct a K×K order square matrix.
优选地，所述采用蚁群算法对Femtocell进行分组包括：Preferably, said adopting ant colony algorithm to group Femtocells includes:
定义毫微微基站FBS的干扰集W＝{wij|i,j∈{1,…,F}}，wij表示FBSi所授权的用户接收到FBSj参考信号功率的平均值，F为FBS的个数；Define the interference set W of femto base station FBS = {wij |i, j ∈ {1,...,F}}, wij represents the average value of the reference signal power of FBSj received by users authorized by FBSi , and F is FBS the number of
定义FBS集合S＝{si|i∈{1,…,F}}，si表示的第i个毫微微基站，分组空间为Groups＝{Gx|x＝1,2,3…,Z}，Gx代表的是特定的分组方式，其中x是以特定的蚂蚁x为起点，其中Z代表的是分组方式的个数；Define the FBS set S={si |i∈{1,...,F}}, the i-th femto base station represented by si , the grouping space is Groups={Gx |x=1,2,3...,Z }, Gx represents a specific grouping method, where x is a specific ant x as the starting point, and Z represents the number of grouping methods;
设置权重系数f1和f2，用于调节期望启发式因子和信息启发式因子；Set the weight coefficients f1 and f2 for adjusting the desired heuristic factor and the information heuristic factor;
计算干扰集W；Calculate the interference set W;
投放R只蚂蚁，形成Z种分组方式，选择W最小的分组方式；Put R ants, form Z kinds of grouping methods, and choose the grouping method with the smallest W;
计算相邻两次分组干扰值的差值ΔW，若ΔW小于一预定义阈值，则通过f1和f2调节期望启发式因子和信息启发式因子并更新全局信息素；Calculate the difference ΔW between two adjacent group interference values, if ΔW is less than a predefined threshold, adjust the desired heuristic factor and information heuristic factor through f1 and f2 and update the global pheromone;
当达到一预定义条件时，计算终止。The calculation is terminated when a predefined condition is reached.
优选地，所述采用启发式算法对FUEs进行信道分配包括：Preferably, said adopting a heuristic algorithm to allocate channels to FUEs includes:
假设每个子信道上的功率平均分配，并将正交的子信道分配给不同的Femtocell分组，同组中FBSs可以复用相同的子信道；Assuming that the power on each subchannel is evenly distributed, and the orthogonal subchannels are allocated to different Femtocell groups, FBSs in the same group can reuse the same subchannel;
在满足各个FUEs速率需求的基础上，最大化系统容量，On the basis of meeting the rate requirements of each FUEs, the system capacity is maximized,
其中，Dj为第j个FBS所服务FUEs的集合，Cl为第k个信道所分配的第l组FBS，约束条件表示所有的子信道都会被分配到某一组中，λk,l表示信道k是否分配给l组，表示FUEs的数据速率需求，是指第j个FBS所服务的FUEn的数据速率需求，K表示子信道数目，B表示信道的带宽，L表示分组的数目，表示信干噪比。Among them, Dj is the set of FUEs served by the j-th FBS, Cl is the l-th group of FBSs allocated by the k-th channel, and the constraints indicate that all sub-channels will be allocated to a certain group, λk,l Indicates whether channel k is assigned to group l, Indicates the data rate requirement of FUEs, refers to the data rate requirement of the FUEn served by the jth FBS, K represents the number of sub-channels, B represents the bandwidth of the channel, L represents the number of groups, Indicates the signal-to-interference-noise ratio.
优选地，所述对FUEs进行功率分配还包括采用分布式功率分配方法：Preferably, said performing power allocation on FUEs also includes adopting a distributed power allocation method:
其中，pij(k)为第j个毫微微基站sj到毫微微用户j在第k步的发射功率，γi(k)表示毫微微用户i在第k步的信干噪比SINR，β是比例系数，γ是设置的SINR阈值。Among them, pij (k) is the transmission power from the jth femto base station sj to femto user j at the kth step, γi (k) represents the SINR of the femto user i at the kth step, β is the scaling factor and γ is the set SINR threshold.
与现有技术比，本发明可以有效降低FBSs对MUEs的干扰，确保MUEs的正常传输，提升整个系统的容量。Compared with the prior art, the present invention can effectively reduce the interference of FBSs to MUEs, ensure the normal transmission of MUEs, and improve the capacity of the whole system.
附图说明Description of drawings
图1本发明用于Macrocell-Femtocell网络中的资源分配方法优选实施例流程图；Fig. 1 present invention is used in the flow chart of preferred embodiment of the resource allocation method in Macrocell-Femtocell network;
图2本发明用于Macrocell-Femtocell网络中的资源分配方法中用于MUEs子信道分配过程的实施例流程图；Fig. 2 is a flow chart of an embodiment of the MUEs subchannel allocation process used in the resource allocation method in the Macrocell-Femtocell network of the present invention;
图3本发明与现有技术中断概率仿真比较图；Fig. 3 present invention and prior art outage probability simulation comparison diagram;
图4本发明与现有技术平均吞吐量比较图；Fig. 4 compares the average throughput between the present invention and the prior art;
图5本发明与现有技术频谱效率仿真比较图；Fig. 5 is a comparison diagram of spectrum efficiency simulation between the present invention and the prior art;
图6本发明与现有技术公平性仿真比较图；Fig. 6 is a comparison diagram of fairness simulation between the present invention and the prior art;
图7本发明与现有技术满意度仿真比较图。Fig. 7 is a simulation comparison diagram of the satisfaction degree between the present invention and the prior art.
具体实施方式detailed description
为使本发明的目的、技术方案和优点表达得更加清楚明白，下面结合附图及具体实施案例对本发明做进一步详细说明。In order to express the object, technical solution and advantages of the present invention more clearly, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation cases.
图1所示为本发明用于Macrocell-Femtocell网络中的资源分配方法优选实施例流程图，该方法包括以下步骤：Fig. 1 shows that the present invention is used in the preferred embodiment flowchart of the resource allocation method in Macrocell-Femtocell network, and this method comprises the following steps:
步骤101、采用差额算法对MUEs执行子信道分配；Step 101, performing sub-channel allocation on MUEs by using a difference algorithm;
步骤102、采用注水算法为每个MUEs子信道分配传输功率；Step 102, using the water filling algorithm to allocate transmission power for each MUEs sub-channel;
步骤103、采用蚁群算法对Femtocell进行分组；Step 103, using the ant colony algorithm to group the Femtocells;
步骤104、采用启发式算法对FUEs进行信道分配；Step 104, using a heuristic algorithm to allocate channels to FUEs;
步骤105、对FUEs进行功率分配。Step 105, perform power allocation on the FUEs.
图2为本发明用于Macrocell-Femtocell网络中的资源分配方法中用于MUEs子信道分配过程的实施例流程图，包括：Fig. 2 is the flow chart of an embodiment of the MUEs subchannel allocation process used in the resource allocation method in the Macrocell-Femtocell network of the present invention, including:
假定MBS在每个子信道上的发射功率是相等的。MUEm在子信道k上的信干噪比SINR计算如下：It is assumed that the transmit power of MBS on each subchannel is equal. The SINR of MUEm on sub-channel k is calculated as follows:
其中，和分别为MBS和FBSj在子信道k上的发射功率。δ＝{1,2,...,K}表示子信道的集合，k∈δ。表示的是MUE的集合，和分别为宏基站和毫微微基站j在确定的子信道k下到MUEm的信道增益，φ＝{1,2,...,F}为FBS的取值范围，σ2是指噪声功率。in, with are the transmit powers of MBS and FBSj on sub-channel k, respectively. δ={1,2,...,K} represents a set of sub-channels, k∈δ. Represents the set of MUE, with are the channel gains of the macro base station and femto base station j to the MUEm under the determined sub-channel k, φ={1,2,...,F} is the value range of FBS, and σ2 refers to the noise power.
结合式(1)，在MUEs满足数据速率需求的条件下，将子信道分配问题规划如下：Combined with formula (1), under the condition that MUEs meet the data rate requirements, the sub-channel allocation problem is planned as follows:
ξm.k∈{0,1} (5)ξmk ∈{0,1} (5)
其中，B表示子信道的带宽，Rm表示MUEm的数据速率需求，ξm,k表示子信道k的分配情况。当子信道k分配给MUEm时，ξm,k为1，否则为0。Among them, B represents the bandwidth of the sub-channel, Rm represents the data rate requirement of MUEm, and ξm,k represents the allocation of sub-channel k. When subchannel k is allocated to MUEm, ξm,k is 1, otherwise it is 0.
在对MUE进行子信道分配前，为MUE在所分配的子信道上设置一个干扰阈值，以保证MUE的正常传输。因此MUEm在给定的子信道k上需要满足：Before allocating subchannels to the MUE, an interference threshold is set for the MUE on the allocated subchannels to ensure normal transmission of the MUE. Therefore, MUEm needs to satisfy on a given subchannel k:
表示宏用户m能在确定的子信道k上能正常传输的一个干扰容限。 Indicates an interference tolerance that macro user m can normally transmit on a certain sub-channel k.
由式(2)知，对MUE进行子信道分配就是M个宏用户与K个子信道之间的一个指派问题。针对MUEs子信道的分配问题，发明人采用了差额算法。对MUEs子信道问题规划如下：From formula (2), it is known that allocating sub-channels to MUE is an assignment problem between M macro users and K sub-channels. Aiming at the allocation problem of MUEs sub-channels, the inventor adopts a difference algorithm. The sub-channel problem planning for MUEs is as follows:
其中，M是指宏用户的总数，K是指子信道的总数。式(7)是需要求解的优化目标函数。为方便理解，本发明假设宏基站在各个子信道上的发射功率是相等的，由式(7)可以知道所需要的目标矩阵该算法如图2所示，具体包括：Wherein, M refers to the total number of macro users, and K refers to the total number of sub-channels. Equation (7) is the optimization objective function that needs to be solved. For the convenience of understanding, the present invention assumes that the transmit power of the macro base station on each sub-channel is equal, and the required target matrix can be known from formula (7) The algorithm is shown in Figure 2, specifically including:
101A：根据所有需要分配子信道的MUEs的信道增益，构建第一次迭代所需的效益矩阵101A: According to the channel gains of all MUEs that need to allocate sub-channels, construct the benefit matrix required for the first iteration
101B：若K<M，即子信道数目小于MUE数目，则添加M-K个虚拟子信道，将目标矩阵变为M×M阶的方阵；101B: If K<M, that is, the number of sub-channels is less than the number of MUEs, then add M-K virtual sub-channels, and change the target matrix into a square matrix of order M×M;
101C：若K>M，即MUE数目大于子信道数目的时候，添加K-M个虚拟MUE，将目标矩阵变为K×K阶的方阵；101C: If K>M, that is, when the number of MUEs is greater than the number of sub-channels, add K-M virtual MUEs to change the target matrix into a square matrix of K×K order;
101D：转化成平衡的指派问题后，利用标准的差额法，得到子信道的分配策略；101D: After transforming into a balanced assignment problem, use the standard difference method to obtain the allocation strategy of sub-channels;
101E：若终止条件得到实现，则立刻结束程序；否则，先将已经分配的子信道从待分配的子信道集合中除去，然后再将满足速率需求的MUE从需要分配子信道的MUE集合中除去，最后利用新的子信道集合和MUE集合构建新的目标矩阵，转至步骤101B开始循环；当所有MUE的速率需求被满足或者所有子信道已经被分配完毕，结束算法。101E: If the termination condition is fulfilled, end the procedure immediately; otherwise, first remove the allocated subchannels from the set of subchannels to be allocated, and then remove the MUE that meets the rate requirement from the set of MUEs that need to allocate subchannels , and finally use the new subchannel set and MUE set to construct a new target matrix, go to step 101B to start the loop; when the rate requirements of all MUEs are met or all subchannels have been allocated, the algorithm ends.
步骤102中为每个子信道分配传输功率，其具体实现方法为：In step 102, the transmission power is allocated for each subchannel, and its specific implementation method is:
在MUEs的子信道分配结束后，用注水算法对一开始平均分配的功率进行重新分配，以便进一步提升系统的容量。对MUEs进行功率分配的规划如下：After the sub-channel allocation of MUEs ends, the water-filling algorithm is used to reallocate the power allocated evenly at the beginning, so as to further improve the capacity of the system. The power allocation plan for MUEs is as follows:
其中，是指在给定的子信道k上的增益干扰比，m是指在子信道分配的时候已经确定了的MUE，pk为子信道k上的功率，Ptot为总的发射功率，条件(10)就是要满足所有子信道上的发射功率不大于总的发射功率。in, Refers to the gain-to-interference ratio on a given sub-channel k, m refers to the MUE that has been determined when the sub-channel is allocated, pk is the power on the sub-channel k, Ptot is the total transmit power, and the condition ( 10) It is to satisfy that the transmit power on all sub-channels is not greater than the total transmit power.
根据上述条件，通过注水算法来进行功率调整。可知，由拉格朗日乘数法得：According to the above conditions, the power adjustment is performed through the water injection algorithm. It can be seen that by the Lagrange multiplier method:
其中，ζ为拉格朗日乘子，是一个常数。计算偏导就可得到每个子信道上的传输功率：Among them, ζ is the Lagrangian multiplier, which is a constant. Calculate partial derivatives The transmission power on each subchannel can be obtained:
其中，η＝B/ζln2为注水线。利用上述方法可快速求出每个子信道上的传输功率，进一步提高系统总的吞吐量。in, η=B/ζln2 is the water injection line. The transmission power on each sub-channel can be quickly calculated by using the above method, and the overall throughput of the system can be further improved.
进一步，对于图1流程图步骤103中描述的FBS分组过程的具体如下：Further, the details of the FBS grouping process described in step 103 of the flow chart of FIG. 1 are as follows:
FBS分组问题与协作学习分组问题模型相同。本文是基于蚁群算法来解决这个分组问题。定义FBS的干扰集W＝{wij|i,j∈{1,…,F}}，F为FBS的个数。wij表示毫微微基站i所授权的用户接收到毫微微基站j参考信号功率的平均值。同理，wji是指毫微微基站j所授权的用户接收到的毫微微基站i参考信号功率的平均值。本文使用两者之中较大的值来表示毫微微基站i与毫微微基站j之间的干扰情况，即wij＝wji＝max(wij wji)。定义FBS的集合S＝{si|i∈{1,…,F}}，分组空间为Groups＝{Gx|x＝1,2,3…,Z}，其中Z代表的是分组方式的个数，每次蚂蚁选择的起始FBS不同，代表一种分组方式Gx，有：The FBS group problem is modeled the same as the collaborative learning group problem. This paper is based on ant colony algorithm to solve this grouping problem. Define the interference set of FBS W={wij |i,j∈{1,...,F}}, F is the number of FBS. wij represents the average value of reference signal power received by users authorized by femto base station i to femto base station j. Similarly, wji refers to the average value of reference signal power of femto base station i received by users authorized by femto base station j. In this paper, the larger value of the two is used to represent the interference between femto base station i and femto base station j, that is, wij =wji =max(wij wji ). Define the set S={si |i∈{1,...,F}} of FBS, and the grouping space is Groups={Gx |x=1,2,3...,Z}, where Z represents the grouping method The number, the starting FBS selected by ants is different each time, which represents a grouping method Gx , which is:
其中K为分组个数。针对分组方式Gx，若sj被分配到了小组则每个FBS都只能被分配到一个组中，不能一个FBS同时在两个组，且所有的FBS都会被分配完，必定在某一个小组中。因此，对于和有：Where K is the number of groups. For the grouping method Gx , if sj is assigned to the group but Each FBS can only be assigned to one group, and one FBS cannot be in two groups at the same time, and all FBS will be assigned, and must be in a certain group. Therefore, for with have:
FBS的分组问题定义为：The grouping problem of FBS is defined as:
W×S×Groups→Gx (16)W×S×Groups→Gx (16)
即针对FBS的分组就是基于干扰W和FBS集合S在分组空间Groups中选择干扰最小的分组方式Gx。问题规划如下：That is, the grouping for the FBS is to select the grouping mode Gx with the least interference in the grouping space Groups based on the interference W and the FBS set S. The problem plan is as follows:
s.t.：s.t.:
在目标函数中，Q值表示给定的分组方式Gx中各组的干扰值之和。约束条件(18)、(19)和(20)保证了F个FBS都会被分配完毕，且只能在一个组中。因此，FBS分组问题就是找到目标函数Q值的最小值，此时的分组方式Gx，就是我们得到的分组结果，分组后的频谱效率会得到显著提高。In the objective function, the Q value represents the sum of the interference values of each group in a given grouping modeGx . Constraints (18), (19) and (20) ensure that F FBSs are all allocated and can only be in one group. Therefore, the FBS grouping problem is to find the minimum value of the objective function Q value. At this time, the grouping method Gx is the grouping result we get, and the spectral efficiency after grouping will be significantly improved.
本文利用蚁群算法，以FBS干扰W作为启发信息，并针对蚁群算法在寻优过程中有可能陷入局部最优的现象，为信息素浓度设置一个最大最小值，限定一个范围，避免初始时信息素浓度为0的路径上一直无蚂蚁寻优，从而降低分组的准确度。并设置两个权重系数f1和f2，对期望启发式因子和信息启发式因子进行动态调节，以改变信息量和期望值的重要程度来对信息素进行更新。算法首先初始化相关参数，计算出F个FBS的干扰W。选择最优分组的过程为：根据蚁群算法的特性，选择R只蚂蚁投放，R只蚂蚁从集合S中选择起始的FBS，并通过状态转移概率公式选择蚂蚁下一步到达的FBS，一直到F个FBS遍历完，每一次选择的起点FBS不同，代表不同的分组方式，在形成的Z种分组方式中，选择干扰总值W最小的分组方式。计算出相邻两次分组干扰值得差值ΔW，如果小于设定的某一阈值，则通过f1和f2来动态调节启发因子和期望因子，再对全局信息素进行更新。当迭代的次数大于设定的最大次数t_max，停止迭代。具体过程如下：(1)初始化参数。初始化相关参数t_max，蚂蚁数R，信息素初始化矩阵MartrixN×N，信息素挥发概率ρ等。This paper uses the ant colony algorithm, uses FBS interference W as the heuristic information, and aims at the phenomenon that the ant colony algorithm may fall into a local optimum during the optimization process, sets a maximum and minimum value for the pheromone concentration, limits a range, and avoids the initial There is no ant optimization on the path with pheromone concentration of 0, which reduces the accuracy of grouping. And set two weight coefficients f1 and f2 to dynamically adjust the expectation heuristic factor and the information heuristic factor to update the pheromone by changing the information amount and the importance of the expected value. The algorithm first initializes relevant parameters and calculates the interference W of F FBSs. The process of selecting the optimal grouping is: according to the characteristics of the ant colony algorithm, select R ants to launch, and R ants select the initial FBS from the set S, and select the FBS that the ants will arrive at next through the state transition probability formula, until After traversing the F FBSs, the starting point FBS selected each time is different, representing different grouping methods. Among the Z grouping methods formed, the grouping method with the smallest total interference value W is selected. Calculate the difference ΔW between two adjacent group interference values, if it is less than a set threshold, dynamically adjust the heuristic factor and expectation factor through f1 and f2 , and then update the global pheromone. When the number of iterations is greater than the set maximum number t_max, stop the iteration. The specific process is as follows: (1) Initialize parameters. Initialize related parameters t_max, number of ants R, pheromone initialization matrix MatrixN×N, pheromone volatilization probability ρ, etc.
(2)蚂蚁投放后，选择蚂蚁出发的起始FBS，用数组p来记录每只蚂蚁出发的FBS编号。建立禁忌表(tabu list)，将被蚂蚁选择过的FBS记录下来，避免在寻优的过程中重复选择同一个FBS。禁忌表记录已选FBS的过程如下：(2) After the ants are released, select the initial FBS from which the ants depart, and use the array p to record the number of the FBS from which each ant departs. Establish a tabu list (tabu list) to record the FBS selected by ants, so as to avoid repeatedly selecting the same FBS during the optimization process. The process of recording the selected FBS in the taboo table is as follows:
①对于投放的蚂蚁，从FBS集合S中随机选取一个si作为蚂蚁出发的初始起点；① For the released ants, randomly select asi from the FBS set S as the initial starting point of the ants;
②如果此时数组p中没有si的编号i，将编号i存储在p中；令p＝[i]；在禁忌表中记录下si的编号i；tabu＝[si]，表示这是以si为起点建立的分组方式Gx；②If there is no number i of si in the array p at this time, store the number i in p; make p=[i]; record the number i of si in the tabu table; tabu=[si ], which means that is the grouping method Gx established starting fromsi ;
③若数组p中有si的编号i，从集合S中重新选取一个毫微微基站sj为蚂蚁出发的起点，再跳转到②进行重新判断。③If there is number i of si in the array p, reselect a femto base station sj from the set S as the starting point for the ant, and then jump to ② for re-judgment.
(3)根据转移概率公式选择路径。在状态转移概率公式中分别加入f1和f2和两个权重系数，当目标函数求解出的相邻的两个解的差值，即求解出的干扰差值ΔW小于设定的阈值q的时候，则通过改变权重系数f1和f2来动态的调节启发因子α和期望因子β，以改变蚂蚁行走中残留信息量和期望值的重要程度。转移概率公式如下：(3) Select the path according to the transition probability formula. Add f1 and f2 and two weight coefficients to the state transition probability formula respectively. When the difference between two adjacent solutions obtained by the objective function, that is, the obtained interference difference ΔW is less than the set threshold q At this time, the heuristic factor α and the expectation factor β are dynamically adjusted by changing the weight coefficients f1 and f2 , so as to change the importance of the amount of residual information and the expected value in the ant walking. The transition probability formula is as follows:
(4)其中，表示在t时刻蚂蚁k由si转移到sj的概率；allowedk表示蚂蚁k在下一步被允许选择的FBS；Martrix(i,j)表示当前路径上的信息素值。(4) Among them, Indicates the probability that ant k transfers from si to sj at time t; allowedk indicates the FBS that ant k is allowed to choose in the next step; Matrix(i,j) indicates the pheromone value on the current path.
(5)蚂蚁将会按照公式(21)选择与当前si干扰较小的sj移动。在其向sj移动的过程中，会对sj进行分组编号，FBS分配组号的过程如下：(5) Ants will choose sj that has less interference with currentsi to move according to formula (21). In the process of moving to sj , sj will be grouped and numbered, and the process of FBS assigning group numbers is as follows:
①如果则下一个毫微微基站r＝1,2,...K r≠y；①If Then the next femto base station r=1,2,...K r≠y;
②否则②Otherwise
③直到每只蚂蚁将所有FBS遍历完，即都被分配完毕，必然在某一组中，此时形成分组空间Groups＝{Gx|x＝1,2,...,M}。③Until each ant has traversed all the FBS, that is have been allocated, must be in a certain group , the grouping space Groups={Gx |x=1,2,...,M} is formed at this time.
(6)更新局部信息素。对于根据W值，按照公式(22)更新局部信息素：(6) Update the local pheromone. for According to the W value, update the local pheromone according to formula (22):
(7)比较中W值得大小，选择W值最小的分组方式Gx作为此次迭代的最优解。(7) compare The W value in the middle is the size, and the grouping method Gx with the smallest W value is selected as the optimal solution for this iteration.
(8)按照公式(23)对全局信息素进行更新，避免蚂蚁残留的信息素过多而淹没启发信息：(8) Update the global pheromone according to the formula (23), so as to avoid too much pheromone left by ants and drown the heuristic information:
Matrix(t+1)＝(1-ρ)×Matrix(t)+Δτ(t) (23)Matrix(t+1)=(1-ρ)×Matrix(t)+Δτ(t) (23)
(9)由于可能出现某些路径一开始没有蚂蚁经过，导致信息素为0，使此条路径一直没有蚂蚁选择，这样分组结果准确度会有所降低。针对此现象，对信息素的最大最小值设置一个范围，既避免分组出现极度失衡的状况，又不影响其在寻优的过程中找优秀解，且又增加了分组方式的多样性。(9) Since there may be some paths that no ants pass through at the beginning, the pheromone is 0, so that this path has not been selected by ants, so the accuracy of the grouping results will be reduced. In response to this phenomenon, a range is set for the maximum and minimum values of pheromones, which not only avoids the extreme imbalance in the grouping, but also does not affect its search for an excellent solution in the process of optimization, and increases the diversity of grouping methods.
(10)如果ΔW<q，则调整f1'＝f1+Δf1，f2'＝f2+Δf2；否则，f1、f2保持不变。(10) If ΔW<q, adjust f1 ′=f1 +Δf1 , f2 ′=f2 +Δf2 ; otherwise, f1 and f2 remain unchanged.
(11)令t＝t+1，当t<t_max时，信息素的增量归0，跳转至步骤(3)进行信息素更新；否则，算法结束，选择此时干扰值最小的分组方式Gbest作为我们的最优解。(11) Let t=t+1, when t<t_max, the increment of pheromone returns to 0, and jumps to step (3) for pheromone update; otherwise, the algorithm ends, and the grouping method with the smallest interference value at this time is selected Gbest is our optimal solution.
进一步，步骤104中利用一种启发式算法对毫微微用户进行信道分配，可在满足毫微微小区用户的数据速率需求下实现，具体过程为：Further, in step 104, a heuristic algorithm is used to allocate channels to femto users, which can be realized while satisfying the data rate requirements of femto cell users, and the specific process is as follows:
假定每个子信道上的功率平均分配。给不同组分配正交的子信道，同组中FBSs可以复用相同的子信道。在满足各个FUEs速率需求的基础上，最大化系统容量，问题规划如下：An even distribution of power on each subchannel is assumed. Orthogonal sub-channels are assigned to different groups, and FBSs in the same group can reuse the same sub-channels. On the basis of meeting the rate requirements of each FUEs, to maximize the system capacity, the problem planning is as follows:
其中，Dj为第j个FBS服务FUEs的集合。是指sj服务的FUEn的数据速率需求。当λk,l等于1的时候，表示子信道k分配给组Cl使用，否则λk,l为0。式(25)表示所有的子信道都会被分配到某一组中。式(26)表示的是毫微微用户的数据速率需求。Wherein, Dj is the set of FUEs served by the jth FBS. refers to the data rate requirement of FUEn served bysj . When λk,l is equal to 1, it means that sub-channel k is allocated to group Cl , otherwise λk,l is 0. Equation (25) indicates that all sub-channels will be assigned to a certain group. Equation (26) expresses the data rate requirement of femto users.
利用一种启发式信道分配算法来对用户进行信道分配，算法流程如下：A heuristic channel allocation algorithm is used to allocate channels to users. The algorithm flow is as follows:
①计算每组的平均速率需求Rl，|Cl|为表示第l组中FBSs的数目。① Calculate the average rate requirement Rl of each group, |Cl | is the number of FBSs in group l.
②确定每组需要分配的子信道数目Nl，②Determine the number Nl of sub-channels to be allocated for each group,
③对于每一个可用子信道，依次计算它在每组的SINR。例如子信道k在第l组的SINR为k∈δ。③ For each available sub-channel, calculate its SINR in each group in turn. For example, the SINR of subchannel k in group l is k ∈ δ.
④确定子信道k在哪一组的SINR最大，如果这组没有分配到足够的子信道将子信道k分配给这组。④Determine which group of subchannel k has the largest SINR, and assign subchannel k to this group if there are not enough subchannels allocated to this group.
⑤更新δ和每组已分配的子信道数。重复步骤③④直到分配完全部的子信道。⑤ Update δ and the number of allocated sub-channels for each group. Repeat steps ③④ until all sub-channels are allocated.
其中，每组中的FBS只能使用自己组所分配到的子信道，以便消除不同组中Femtocell之间的干扰。Wherein, the FBS in each group can only use the sub-channel allocated by its own group, so as to eliminate the interference between Femtocells in different groups.
步骤105中对毫微微用户进行功率分配，包括利用分布式功率分配算法，其具体实现过程为：In step 105, performing power allocation to femto users includes using a distributed power allocation algorithm, and its specific implementation process is:
功率分配按下式进行：The power distribution is carried out as follows:
其中，pij(k)为sj到毫微微用户j在第k步的发射功率。γi(k)表示毫微微用户i在第k步的SINR。β是比例系数，取值范围为(0,1]。γ是设置的SINR阈值，毫微微用户的SINR平衡于γ或γ之上，以避免影响MUE的QoS。Among them, pij (k) is the transmission power from sj to femto user j at step k. γi (k) represents the SINR of femto user i at step k. β is a proportional coefficient, and its value range is (0,1]. γ is a set SINR threshold, and the SINR of the femto user is balanced on γ or above γ to avoid affecting the QoS of the MUE.
在系统仿真时具体所用参数见表1，本文的信道增益主要考虑路径损耗、阴影衰落、穿墙损耗和天线增益。The specific parameters used in system simulation are shown in Table 1. The channel gain in this paper mainly considers path loss, shadow fading, wall penetration loss and antenna gain.
表1仿真参数Table 1 Simulation parameters
图3显示了MUE在室内比例为10％-100％下的中断概率。在仿真中，FBS的部署密度为100％并且将-6dB设定为中断阈值。由仿真图可知，随着室内MUE的比例增加，MUE的中断概率在不断提升。这是因为室内MUE与宏基站之间的信道条件较差且受到的FBS的干扰很严重，导致室内MUE的信道质量难以保证。本文所述算法的中断概率性能与RRA算法相比，变得越来越优越。因为本文算法通过避免FBS与附近MUE使用相同子信道和降低发射功率等方式有效地降低了FBS对MUE的干扰，保证了MUE的最低SINR需求。从图中可以看出，随着MUEs在室内的比例增加，RRA算法得到的MUEs中断概率一直增加到接近100％，但是所提算法得到的MUEs中断概率一直在10％以下。因此，本文算法相比其它算法能更好地消除FBSs对MUEs的干扰，确保MUEs的正常传输。Fig. 3 shows the outage probability of MUE under the indoor ratio of 10%-100%. In the simulation, the deployment density of FBS is 100% and -6dB is set as the outage threshold. It can be seen from the simulation figure that as the proportion of indoor MUEs increases, the outage probability of MUEs continues to increase. This is because the channel condition between the indoor MUE and the macro base station is poor and the interference from the FBS is serious, which makes it difficult to guarantee the channel quality of the indoor MUE. Compared with the RRA algorithm, the outage probability performance of the algorithm described in this paper becomes more and more superior. Because the algorithm in this paper effectively reduces the interference of FBS to MUE by preventing FBS from using the same sub-channel as nearby MUE and reducing the transmission power, which ensures the minimum SINR requirement of MUE. It can be seen from the figure that as the proportion of MUEs in the room increases, the outage probability of MUEs obtained by the RRA algorithm increases to nearly 100%, but the outage probability of MUEs obtained by the proposed algorithm is always below 10%. Therefore, compared with other algorithms, the algorithm in this paper can better eliminate the interference of FBSs to MUEs and ensure the normal transmission of MUEs.
图4描述了显示了室内MUE在FBS部署密度为10％-100％下的平均吞吐量。图中显示，随着部署的Femtocell密度的增加，MUE的平均吞吐量在不断下降。这是因为MUE受到的跨层干扰是随着部署的Femtocell密度变大而增加的。图中最大载干比算法、改进的差额法和RRA算法的吞吐量是在不进行任何干扰管理的情况下得到的。就这一项性能而言，改进的差额法比最大载干比算法要差一些，但是它考虑了MUE间的公平性，能满足更多MUE的正常传输。本文算法1是在利用改进的差额算法对MUE分配子信道的基础上，又加入了对其功率的调整。从仿真性能曲线可知通过功率调整可以使系统的性能得到提高。本文算法2是采用了本文所提的干扰管理策略，有效地降低了FBSs对MUEs的干扰，从而提升了整个系统的性能。Fig. 4 shows the average throughput of indoor MUE under the FBS deployment density of 10%-100%. The figure shows that the average throughput of the MUE keeps decreasing as the density of deployed Femtocells increases. This is because the cross-layer interference received by the MUE increases as the density of deployed Femtocells increases. The throughputs of the maximum carrier-to-interference ratio algorithm, the improved difference method and the RRA algorithm in the figure are obtained without any interference management. As far as this performance is concerned, the improved difference method is worse than the maximum carrier-to-interference ratio algorithm, but it considers the fairness among MUEs and can satisfy the normal transmission of more MUEs. Algorithm 1 in this paper is based on the use of the improved difference algorithm to allocate sub-channels to the MUE, and also adds the adjustment of its power. It can be seen from the simulation performance curve that the performance of the system can be improved through power adjustment. Algorithm 2 in this paper adopts the interference management strategy proposed in this paper, which effectively reduces the interference of FBSs to MUEs, thereby improving the performance of the entire system.
图5描述了FBSs在不同部署密度下，femtocell的频谱效率变化。本文算法1是针对FBS提出的一种分组算法。与另外几种算法相比，算法1能更好地消除FBS之间的干扰，提高了FUE的SINR，从而使得频谱效率得到提升。由于每个组中的FBSs数目不均等，通过组内正交分组后，导致频带不能被充分的利用，从而降低了频谱效率。HCFM降低了FFI，但没有考虑Macrocell-Femtocell之间的跨层干扰，以保证MUEs的服务质量。本文算法2是在算法1对FBS分组的基础上又加入了对其功率的调整，进一步提升了整个系统的容量。Figure 5 depicts the spectrum efficiency variation of femtocells under different deployment densities of FBSs. Algorithm 1 in this paper is a grouping algorithm proposed for FBS. Compared with other algorithms, Algorithm 1 can better eliminate the interference between FBSs, improve the SINR of FUE, and thus improve the spectral efficiency. Since the number of FBSs in each group is not equal, the frequency band cannot be fully utilized after orthogonal grouping within the group, thereby reducing the spectrum efficiency. HCFM reduces FFI, but does not consider the cross-layer interference between Macrocell-Femtocell to guarantee the service quality of MUEs. Algorithm 2 in this paper adds power adjustment to the FBS grouping in Algorithm 1, further improving the capacity of the entire system.
图6描述了FUEs之间的公平性[16]。图中显示，随着Femtocell部署密度的增加，FUE间的公平性不断下降。这是因为随着部署的Femtocell密度的增大，FUE信道质量的差别也变得越来越大。本文算法1是本章所提分组算法。可以看出，本文算法1得到的FUE间的公平性性能只比本文算法2差一点。其它分组算法分得的各个组中的FBS数目不均衡，导致不同组中的FUE受到的干扰差别较大。未分组算法RRA进行随机分配，一些FUE可能受到的干扰更加严重，导致SINR相对较低。Hopfield算法在FBSs密集部署的时候，分组容易陷入局部最优，分组准确度不高。本文算法2在算法1对FBS分组的基础上进行功率调整，进一步减少干扰并提高FUE的SINR。Figure 6 depicts the fairness among FUEs [16]. The figure shows that with the increase of Femtocell deployment density, the fairness among FUEs decreases continuously. This is because as the density of deployed Femtocells increases, the difference in FUE channel quality becomes larger and larger. Algorithm 1 in this paper is the grouping algorithm proposed in this chapter. It can be seen that the fairness performance between FUEs obtained by Algorithm 1 in this paper is only slightly worse than Algorithm 2 in this paper. The number of FBSs in each group assigned by other grouping algorithms is unbalanced, resulting in a large difference in interference received by FUEs in different groups. The ungrouped algorithm RRA performs random allocation, and some FUEs may receive more serious interference, resulting in a relatively low SINR. When the Hopfield algorithm is densely deployed in FBSs, the grouping is easy to fall into local optimum, and the grouping accuracy is not high. In this paper, Algorithm 2 adjusts power based on Algorithm 1 for FBS grouping, further reducing interference and improving SINR of FUE.
FUEs的满意度如图7所示，对比其他算法，只有本文所提算法能使FUEs的满意度保持在一个较高水平。本文所提分组算法是一个迭代寻优过程，可以根据FBSs部署密度自适应地调整每个组中的FBSs数目，分组性能不断提高，能够更好地消除干扰。本文算法2是在本文算法1针对FBS分组的基础上进行功率调整，使得更多FUEs能满足速率需求。The satisfaction of FUEs is shown in Figure 7. Compared with other algorithms, only the algorithm proposed in this paper can keep the satisfaction of FUEs at a high level. The grouping algorithm proposed in this paper is an iterative optimization process, which can adaptively adjust the number of FBSs in each group according to the deployment density of FBSs. The grouping performance is continuously improved, and interference can be better eliminated. Algorithm 2 in this paper is to adjust the power based on Algorithm 1 in this paper for FBS grouping, so that more FUEs can meet the rate requirements.
本发明能够有效地抑制宏小区用户层和毫微微小区用户层之间的跨层干扰和同层干扰，有效地提升网络频谱效率，保证FUEs和MUEs的QoS。The invention can effectively suppress the cross-layer interference and the same-layer interference between the user layer of the macro cell and the user layer of the femto cell, effectively improve the spectrum efficiency of the network, and ensure the QoS of the FUEs and the MUEs.
本发明所举实施方式或者实施例对本发明的目的、技术方案和优点进行了进一步的详细说明，所应理解的是，以上所举实施方式或者实施例仅为本发明的优选实施方式而已，并不用以限制本发明，凡在本发明的精神和原则之内对本发明所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The implementation modes or examples of the present invention further describe the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above implementation modes or examples are only preferred implementation modes of the present invention. It is not intended to limit the present invention, and any modification, equivalent replacement, improvement, etc. made to the present invention within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
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| CN201710145977.6ACN106604401B (en) | 2017-03-13 | 2017-03-13 | Resource allocation method in heterogeneous network |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710145977.6ACN106604401B (en) | 2017-03-13 | 2017-03-13 | Resource allocation method in heterogeneous network |
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| Application Number | Title | Priority Date | Filing Date |
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| Country | Link |
|---|---|
| CN (1) | CN106604401B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107567047A (en)* | 2017-09-28 | 2018-01-09 | 北京邮电大学 | A kind of load-balancing method based on network traffics temporal and spatial orientation in heterogeneous network |
| CN107613565A (en)* | 2017-08-30 | 2018-01-19 | 东莞理工学院 | A radio resource management method in a full-duplex ultra-dense network |
| CN107809795A (en)* | 2017-11-06 | 2018-03-16 | 重庆邮电大学 | Anti-interference method based on time reversal in D2D heterogeneous wireless communication networks |
| CN108848562A (en)* | 2018-05-24 | 2018-11-20 | 重庆邮电大学 | Based on the resource allocation methods of one-to-many matching in heterogeneous network |
| CN108900980A (en)* | 2018-09-21 | 2018-11-27 | 北京邮电大学 | A kind of resource allocation optimization method based on mobility prediction in heterogeneous network |
| CN108924934A (en)* | 2018-07-06 | 2018-11-30 | 西安电子科技大学 | Heterogeneous network interference management method based on multi dimensional resource distribution |
| CN109413570A (en)* | 2018-10-19 | 2019-03-01 | 西北师范大学 | A kind of heterogeneous network uplink spectrum distribution method |
| CN110072275A (en)* | 2018-01-24 | 2019-07-30 | 华北电力大学 | A kind of piconet network power allocation scheme based on water flood |
| TWI700951B (en)* | 2018-10-12 | 2020-08-01 | 瑞昱半導體股份有限公司 | Resource allocation method and full duplex communication system |
| CN111556575A (en)* | 2020-04-27 | 2020-08-18 | 中国电力科学研究院有限公司 | Novel power grid resource allocation system and communication method |
| US12035144B2 (en) | 2019-01-29 | 2024-07-09 | Sony Group Corporation | Spectrum management device, electronic device, radio communication method, and storage medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105490794A (en)* | 2015-12-15 | 2016-04-13 | 重庆邮电大学 | Packet-based resource distribution method for orthogonal frequency division multiple access (OFDMA) femtocell double-layer network |
| CN106231610A (en)* | 2016-09-30 | 2016-12-14 | 重庆邮电大学 | Resource allocation methods based on sub-clustering in Femtocell double-layer network |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105490794A (en)* | 2015-12-15 | 2016-04-13 | 重庆邮电大学 | Packet-based resource distribution method for orthogonal frequency division multiple access (OFDMA) femtocell double-layer network |
| CN106231610A (en)* | 2016-09-30 | 2016-12-14 | 重庆邮电大学 | Resource allocation methods based on sub-clustering in Femtocell double-layer network |
| Title |
|---|
| GANG NING；QINGHAI YANG；YYUNG SUP KWAK；LAJOS HANZO: "Macro- and femtocell interference mitigation in OFDMA wireless systems", 《2012 IEEE GLOBAL COMMUNICATIONS CONFERENCE》* |
| 张海波、穆立雄、陈善学、彭焦阳: "OFDMA毫微微小区双层网络中基于分组的资源分配", 《电子与信息学报》* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107613565A (en)* | 2017-08-30 | 2018-01-19 | 东莞理工学院 | A radio resource management method in a full-duplex ultra-dense network |
| CN107613565B (en)* | 2017-08-30 | 2020-09-04 | 东莞理工学院 | Wireless resource management method in full-duplex ultra-dense network |
| CN107567047B (en)* | 2017-09-28 | 2019-10-08 | 北京邮电大学 | Load-balancing method based on network flow temporal and spatial orientation in a kind of heterogeneous network |
| CN107567047A (en)* | 2017-09-28 | 2018-01-09 | 北京邮电大学 | A kind of load-balancing method based on network traffics temporal and spatial orientation in heterogeneous network |
| CN107809795A (en)* | 2017-11-06 | 2018-03-16 | 重庆邮电大学 | Anti-interference method based on time reversal in D2D heterogeneous wireless communication networks |
| CN110072275B (en)* | 2018-01-24 | 2020-12-29 | 华北电力大学 | A kind of transmission power allocation method applied in femtocell network |
| CN110072275A (en)* | 2018-01-24 | 2019-07-30 | 华北电力大学 | A kind of piconet network power allocation scheme based on water flood |
| CN108848562A (en)* | 2018-05-24 | 2018-11-20 | 重庆邮电大学 | Based on the resource allocation methods of one-to-many matching in heterogeneous network |
| CN108848562B (en)* | 2018-05-24 | 2022-05-31 | 重庆邮电大学 | A Resource Allocation Method Based on One-to-Many Matching in Heterogeneous Networks |
| CN108924934A (en)* | 2018-07-06 | 2018-11-30 | 西安电子科技大学 | Heterogeneous network interference management method based on multi dimensional resource distribution |
| CN108924934B (en)* | 2018-07-06 | 2021-08-20 | 西安电子科技大学 | Interference management method for heterogeneous networks based on multi-dimensional resource allocation |
| CN108900980A (en)* | 2018-09-21 | 2018-11-27 | 北京邮电大学 | A kind of resource allocation optimization method based on mobility prediction in heterogeneous network |
| TWI700951B (en)* | 2018-10-12 | 2020-08-01 | 瑞昱半導體股份有限公司 | Resource allocation method and full duplex communication system |
| CN109413570A (en)* | 2018-10-19 | 2019-03-01 | 西北师范大学 | A kind of heterogeneous network uplink spectrum distribution method |
| US12035144B2 (en) | 2019-01-29 | 2024-07-09 | Sony Group Corporation | Spectrum management device, electronic device, radio communication method, and storage medium |
| CN111556575A (en)* | 2020-04-27 | 2020-08-18 | 中国电力科学研究院有限公司 | Novel power grid resource allocation system and communication method |
| CN111556575B (en)* | 2020-04-27 | 2022-01-14 | 中国电力科学研究院有限公司 | Novel power grid resource distribution system and communication method |
| Publication number | Publication date |
|---|---|
| CN106604401B (en) | 2020-03-13 |
| Publication | Publication Date | Title |
|---|---|---|
| CN106604401B (en) | Resource allocation method in heterogeneous network | |
| CN112601284B (en) | Downlink multi-cell OFDMA resource allocation method based on multi-agent deep reinforcement learning | |
| CN106231610B (en) | Cluster-based resource allocation method in Femtocell dual-layer network | |
| CN106454850B (en) | Resource Allocation Method for Energy Efficiency Optimization of Cellular Heterogeneous Networks | |
| CN107466099B (en) | Interference management self-optimization method based on non-orthogonal multiple access | |
| CN103856947B (en) | An Interference Coordination Method with Joint Channel Selection and Power Control | |
| CN108322938B (en) | Power distribution method based on double-layer non-cooperative game theory under ultra-dense networking and modeling method thereof | |
| Dai et al. | The multi-objective deployment optimization of UAV-mounted cache-enabled base stations | |
| CN106507393B (en) | Access selection method based on comprehensive overhead function | |
| CN107708157A (en) | Intensive small cell network resource allocation methods based on efficiency | |
| CN105490794B (en) | The packet-based resource allocation methods of the Femto cell OFDMA double-layer network | |
| CN102665219B (en) | Dynamic frequency spectrum allocation method of home base station system based on OFDMA | |
| CN104168653B (en) | A kind of macro base station based on interference management and Home eNodeB federated resource distribution method | |
| CN105578482B (en) | A kind of cellular heterogeneous network resource allocation method | |
| CN118828603A (en) | A method for user association and resource allocation in cellular networks based on deep reinforcement learning | |
| CN108449149B (en) | A matching game-based resource allocation method for energy harvesting small base stations | |
| Lu et al. | Power control based time-domain inter-cell interference coordination scheme in DSCNs | |
| CN107517464B (en) | A Method for Interference Management and Resource Allocation in Heterogeneous Networks | |
| CN108307510A (en) | A kind of power distribution method in isomery subzone network | |
| CN108834158B (en) | Interference management method for ultra-dense networking | |
| CN115833886B (en) | A power control method for non-cellular massive MIMO system | |
| CN103945514B (en) | Fairness-based diffuse adaptive uplink power control method | |
| CN107613554B (en) | Interference sensing distributed robust downlink power control method | |
| CN106102151A (en) | The interference management method controlled based on channel distribution and power in family base station system | |
| Jiang et al. | A power adjustment based eICIC algorithm for hyper-dense HetNets considering the alteration of user association |
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