LTE中femtocell的联合信道与功率控制算法

随着LTE中家庭基站的密集部署,会使家庭基站间产生严重的同层干扰,与此同时也会对边缘宏用户产生严重的跨层干扰。针对这两种下行干扰,提出了一种基于图论的信道分配和基于路损的功率控制算法相结合的方案。首先基于图论的知识构建干扰图,通过干扰图进行信道分配,有干扰的小区采用相互正交的信道,降低家庭小区间的同层干扰。然后在保证宏用户和家庭用户QoS的条件下,根据设定的信干噪比(SINR)门限值,设置femtocell干扰标志,并根据信道质量(CQI)设置干扰级别,通过干扰级别进行对应的功率控制,最终达到消除家庭小区对宏用户跨层干扰的目的。仿真结果表明,该算法很好的提高了用户的SINR和吞吐量,同时保证了家庭用户和宏用户的QoS。     

LTE系统多小区上行联合功控算法

针对传统的小区内开环功率控制算法通常以提升本小区的吞吐量性能为目标，忽略了当前小区用户对邻小区用户同频干扰的问题，为提升边缘用户性能的同时兼顾系统整体性能，提出了一种LTE系统小区间上行联合功率控制（UJPC）算法。该算法采用单基站三扇区为系统模型，以最优化系统吞吐量比例公平函数为目标，首先根据最小信干噪比（SINR）约束值和用户最大发射功率这两个约束条件得到相应的数学优化模型，然后采用连续凸近似的方法求解优化问题得出各个基站所管辖的小区内所有用户的最优发射功率。仿真结果表明，与基准的开环功控方案相比，联合功控方案在保证系统平均频谱利用率的情况下能够较大幅度地提高小区边缘频谱利用率，其最佳性能增益能达到50%。     

密集家庭基站网络中基于分簇的资源分配方案

针对家庭基站密集部署情况下的下行干扰问题,提出一种基于分簇的资源分配方案。首先,采用部分频率复用（FFR）技术将网络中所有小区划分成不同的空间,既能抑制宏基站之间的同层干扰,又能降低边缘区域宏基站与家庭基站间的跨层干扰;然后,结合图论的知识及凸优化理论对家庭基站进行分簇,并采用基于用户速率公平的信道分配算法对家庭基站进行子信道分配,抑制家庭基站间的同层干扰;最后,采用分布式功率控制算法对家庭基站功率进行动态调整,进一步提升系统的性能。仿真结果表明：相比传统未分组算法,所提算法的信干噪比（SINR）和吞吐量有明显提高,其中,系统吞吐量低于4 Mb/s的概率降低为30%;同时,与未分组算法相比,所提算法公平性提高了12%,使用户获得更高的满意度。     

多点协作系统中基于PMI反馈的小区间干扰弱化

针对LTE通信系统中小区间干扰,给出多点协作情况下基于预编码矩阵索引反馈(PMI)的小区间干扰弱化方案。干扰弱化方案采用基于码本的预编码技术,接收机选用最小均方差(MMSE)设计,干扰小区间采用轮询调度算法。仿真结果表明,引入PMI反馈协调方案后,多点协作系统中每个用户的平均频谱利用率明显提高,小区间干扰得到有效的抑制。     

认知无线电系统中消除异步干扰的最小均方预编码方法

由于信道状态信息的时变特性和信道反馈误差的共同影响,使得以前波束成形算法无法完全消除小区间的干扰,特别是异步干扰,从而造成小区边缘用户传输速率和服务质量的下降。针对此问题,通过对信道时变特性的研究和反馈误差统计特性的研究,提出了一种考虑信道时变统计特性的基于块对角化的波束成形方案,此方案在基站端对所发送的信号进行编码,对将要造成的异步干扰进行预消除,从而达到提高小区边缘用户服务质量的效果。仿真结果表明,此方案有效抑制了由信道时变引起的干扰,提高了系统的容量。     

基于干扰抑制的上行多基站协作能效算法

针对上行链路多基站协作通信系统中联合处理功耗过高的问题,提出了一种基于小区间干扰抑制的上行多基站协作能效算法(ICIR-UMBCEEA)。首先,通过解码参考信号(DMRS)序列得到等效噪声和协作多点(CoMP)估计信道,推导出CoMP信道的干扰抑制合并(IRC)滤波矩阵;其次,建立等效干扰模型,利用IRC滤波矩阵得到了小区间平均干扰;最后,计算出各小区用户对非CoMP集合小区的干扰程度,针对强干扰用户作出联合处理。理论分析和链路仿真表明,与上行协作多基站最优注水功率控制算法(UMBCA-OWFC)相比,ICIR-UMBCEEA的用户归一化平均干扰在中心用户和边缘用户处的下降幅度分别为19.2%和24.5%;而ICIR-UMBCEEA的能量利用效率在中心用户和边缘用户处分别提高了25.48%和18.03%;ICIR-UMBCEEA所消耗的能量更小,其中心用户的吞吐量更高,而边缘用户的吞吐量则与UMBCA-OWFC相差不大。实验结果表明,ICIR-UMBCEEA在实际工程中能够有效提高系统的能量效率。     

异构网络下行链路层间协作功率控制算法研究

为了克服Femtocell网络的大规模部署所带来的干扰问题,采用对Femtocell基站发送功率控制的方法。针对Femtocell网络边缘Macrocell用户所受到的强下行链路跨层干扰,通过比较Femtocell用户和Macrocell用户的信干噪比(SINR)与最小SINR阈值的关系,提出了基于Femtocell用户路径损耗的Femtocell基站发送功率自适应调整算法,从而提高了Macrocell用户的通信质量。通过仿真验证,该方法使得Macrocell用户所受干扰明显降低,整体网络性能得以提升。     

LTE家庭基站部署中的下行功率控制

家庭基站在LTE中的密集部署,重叠覆盖会在家庭基站间造成较强的干扰。针对家庭基站的下行干扰,提出了一种基于路损的功率控制算法。该算法以下属用户的路损和相邻基站的非下属用户最小路损作为设定基站初始发射功率的依据,用户计算各自的信干噪比（SINR）,并将信干噪比映射成信道质量指示（CQI）发送给家庭基站,各基站根据反馈信息调整自身发射功率,充分考虑到了自身发射功率不足和外界干扰两种情况。仿真结果表明,该算法很好地控制了用户的SINR,同时有效提高了家庭基站用户的吞吐量。     

OFDM中基于干扰协调的上行多点协作传输方法

在LTE系统中正交频分多址(OFDMA)能在所有小区中复用完整的频段,因而能提供很高的频谱效率,但它产生了很高的小区间干扰尤其是在小区边缘。文中提出了一种基于干扰协调(ICIC)的上行多点协作传输(CoMP)方法,该方案结合基于软频率复用的干扰协调算法,不需要为CoMP单独划分额外的带宽。仿真结果表明,相比不采用干扰协调,该方案能够很好地提升用户平均吞吐量和边缘用户的吞吐量。     

基于改进HSARSA(λ)算法的功率控制研究

在MBS-PBS两层异构网络中,微微基站采用小区范围扩展技术对网络进行负载均衡时,pico小区边缘用户的通信受到MBS基站较大干扰。为此,提出一种基于启发函数的改进HSARSA(λ)算法。采用缩减功率的RP-ABS子帧技术,在保证宏基站自身通信性能的同时减小MBS基站对pico边缘用户的干扰,并运用基于启发函数的改进HSARSA(λ)算法与环境进行交互,以配置RP-ABS子帧密度与功率大小,达到干扰协调的目的。仿真结果表明,改进HSARSA算法与原始SARSA和Q-Learning等算法相比,pico边缘用户吞吐量分别提升12%和40%,系统用户吞吐量分别提升10.3%和20.2%,有效提高了pico边缘用户的通信性能。     

干扰受限系统的认知用户功率控制算法

根据认知无线电系统中认知用户的不同通信需求,结合干扰温度模型和非合作博弈理论,以单小区CDMA为系统平台,提出基于SINR的对数效用函数的干扰受限认知无线电系统功率控制算法。仿真实验结果表明,与经典SINR平衡算法及Koskie-Gajic算法相比,该算法在满足认知用户目标SINR和主用户干扰温度限制的前提下,通过适当增大认知用户发射功率,能够满足认知用户高速数据通信的需要。     

多用户MIMO系统中机会波束截断的信道反演

针对下行链路多用户MIMO系统,提出了一种简单的基于机会波束截断的信道反演方法。机会波束形成可以用最小的反馈获得MIMO系统慢衰落信道中下行链路的多用户分集增益和复用增益。在机会波束形成进行自适应信道截断的基础上,使各通信用户的信干噪比（SINR）相同,改善了系统的误比特率性能。仿真结果表明,总的用户数为30时,系统误比特率性能提高3~4 dB。信道反演比注水功率分配简单,降低了系统复杂度。     

认知MIMO网络中增强型干扰对齐算法

针对认知多输入多输出（MIMO）网络中传统基于最大信干噪比的干扰对齐算法,在发送多数据流时随着信噪比的增加不易收敛以及数据流之间的干扰突出的问题,提出一种充分考虑数据流间干扰并进行迭代限制的干扰对齐算法。首先,次用户通过编码设计消除主次间的干扰;然后,在消除主用户之间和次用户之间干扰时,根据信道互易性,运用广义瑞利熵计算基于最大信干噪比算法的预编码与干扰抑制矩阵,并在迭代过程中,每次迭代始终使预编码与干扰抑制矩阵先满足干扰功率在期望信号空间最小;最后,结合次用户间MIMO干扰信道、主次用户间构成的MIMO干扰信道以及次用户网络干扰对齐的必要性,推导出次用户可达自由度上限。实验结果表明,相比传统最大信干噪比算法,所提算法在信噪比较低时次用户总容量无明显提高,但随着信干噪比的增加其优势越来越明显;当达到收敛时,所提算法迭代次数比传统最大信干噪比算法约减少40%。因此,所提算法能够提高系统容量且加快收敛。     

基于多对一Gale-Shapley算法的D2D通信资源分配

针对D2D通信复用异构蜂窝网络上行信道产生的干扰问题和频谱资源优化问题进行研究,提出一种基于多对一Gale-Shapley算法的D2D通信资源分配方案。本方案允许多个D2D用户共享一个蜂窝用户信道资源,通过设置信干噪比（SINR）门限保证用户的通信服务质量（QOS）。根据信道分配情况,构建D2D用户和信道的偏好列表,最大化系统总容量。仿真结果表明,该方案收敛较快,复杂度较低,能够有效保证用户的通信服务质量,系统总容量接近最优解。本研究为实现D2D用户和蜂窝用户的频谱资源共享,提高频谱利用率提供了一种有效方案。     

Coverage probability driven dynamic spectrum allocation in heterogeneous wireless networks

In this paper,we design a dynamic spectrum allocation(DSA) scheme for heterogeneous cellular wireless networks with special interest on guaranteeing the cell coverage probability.To this end,considering users spatial distribution,we propose a new interference control(IC) model,which guarantees SINR(signal to interference plus noise ratio) requirements of different services and ensures the coverage performance of base stations(BSs).Under such an IC model,we formulate the DSA scheme as a combinatorial optimization problem.Since the problem is computationally intractable,we design an algorithm for its solution based on graph coloring.Simulation results indicate that the proposed DSA scheme can increase the total spectrum utility while effectively controlling the interference among BSs and meeting SINR requirements of users.     

Resource allocation for physical-layer security in OFDMAdownlinkwith imperfect CSI

We investigate the problem of resource allocation in a downlink orthogonal frequency-division multiple access (OFDMA) broadband network with an eavesdropper under the condition that both legitimate users and the eavesdropper are with imperfect channel state information (CSI). We consider three kinds of imperfect CSI: (1) noise and channel estimation errors, (2) feedback delay and channel prediction, and (3) limited feedback channel capacity, where quantized CSI is studied using rate-distortion theory because it can be used to establish an informationtheoretic lower bound on the capacity of the feedback channel. The problem is formulated as joint power and subcarrier allocation to optimize the maximum-minimum (max-min) fairness criterion over the users’ secrecy rate. The problem considered is a mixed integer nonlinear programming problem. To reduce the complexity, we propose a two-step suboptimal algorithm that separately performs power and subcarrier allocation. For a given subcarrier assignment, optimal power allocation is achieved by developing an algorithm of polynomial computational complexity. Numerical results show that our proposed algorithm can approximate the optimal solution.     

Resource allocation algorithm with limited feedback for multicast single frequency networks

The single frequency network (SFN) can provide a multimedia broadcast multicast service over a large coverage area. However, the application of SFN is still restricted by a large amount of feedback. Therefore, we propose a multicast resource allocation scheme based on limited feedback to maximize the total rate while guaranteeing the quality of service (QoS) requirement of real-time services. In this scheme, we design a user feedback control algorithm to effectively reduce feedback load. The algorithm determines to which base stations the users should report channel state information. We then formulate a joint subcarrier and power allocation issue and find that it has high complexity. Hence, we first distribute subcarriers under the assumption of equal power and develop a proportional allocation strategy to achieve a tradeoff between fairness and QoS. Next, an iterative water-filling power allocation is proposed to fully utilize the limited power. To further decrease complexity, a power iterative scheme is introduced. Simulation results show that the proposed scheme significantly improves system performance while reducing 68% of the feedback overhead. In addition, the power iterative strategy is suitable in practice due to low complexity.     

潜铺型卫星认知通信中上行链路功率控制

针对卫星通信中存在有效信道远小于注册频率的情况,提出了以潜铺型认知无线电为技术依靠的卫星上行链路功率控制算法。该算法以次要用户所获吞吐量与付出代价之差为效用函数,通过次要用户作为参与者建立的博弈模型进行纳什均衡求解,得到最优功率分配策略。该策略可满足次要用户自身需求,亦不影响主要用户系统正常通信,能有效提高频带使用率。在性能方面,指出了次要用户系统容量和预留信噪比的关系。仿真结果表明,在主要用户系统容许范围内次要用户数量越多则其系统吞吐量和系统收益越大,最后讨论了算法的实现复杂度。     

Energy-efficient cell-association bias adjustment algorithm for ultra-dense networks

In recent years, energy efficiency has become an important topic, especially in the field of ultra-dense networks (UDNs). In this area, cell-association bias adjustment and small cell on/off are proposed to enhance the performance of energy efficiency in UDNs. This is done by changing the cell association relationship and turning off the extra small cells that have no users. However, the variety of cell association relationships and the switching on/off of the small cells may deteriorate some users’ data rates, leading to nonconformance to the users’ data rate requirement. Considering the discreteness and non-convexity of the energy efficiency optimization problem and the coupled relationship between cell association and scheduling during the optimization process, it is difficult to achieve an optimal cell-association bias. In this study, we optimize the network energy efficiency by adjusting the cell-association bias of small cells while satisfying the users’ data rate requirement. We propose an energy-efficient centralized Gibbs sampling based cell-association bias adjustment (CGSCA) algorithm. In CGSCA, global information such as channel state information, cell association information, and network load information need to be collected. Then, considering the overhead of the messages that are exchanged and the implementation complexity of CGSCA to obtain the global information in UDNs, we propose an energy-efficient distributed Gibbs sampling based cell-association bias adjustment (DGSCA) algorithm with a lower message-exchange overhead and implementation complexity. Using DGSCA, we derive the updated formulas for calculating the number of users in a cell and the users’ SINR. We analyze the implementation complexities (e.g., computation complexity and communication com- plexity) of the proposed two algorithms and other existing algorithms. We perform simulations, and the results show that CGSCA and DGSCA have faster convergence speed, as well as a higher performance gain of the energy efficiency and throughput compared to other existing algorithms. In addition, we analyze the importance of the users’ data rate constraint in optimizing the energy efficiency, and we compare the energy efficiency performance of different algorithms with different number of small cells. Then, we present the number of sleeping small cells as the number of small cells increases.