多用户MIMO系统的一种低复杂度用户选择算法

联合块对角化和几何均值分解（BD-GMD）是一种用于下行多用户多输入多输出（MU-MIMO）系统的预编码方案。在信噪比（SNR）较高的情况下,此方案在信道吞吐量和误符号率（SER）两方面都是渐进最优的。但是,在BD-GMD方案中,发射和接收天线的数量限制了能同时通信的用户数。在一个拥有大量用户的下行MU-MIMO系统中,为了使得系统总的吞吐量最大,基站必须从大量的用户中选出一个用户子集,同时对他们进行服务。采用穷尽搜索算法能得到最优的用户子集,但这种方法所需要的计算量太大。因此,我们提出一种次最优但是计算复杂度较低的用户选择算法,用于采用BD-GMD方案的下行MU-MIMO系统。此算法具有随用户数变化的线性复杂度,另外,仿真结果显示,其总的吞吐量大致能达到采用穷尽搜索算法所获得的系统总吞吐量的95％。     

块对角化预编码下的用户选择技术

针对下行多用户多天线系统(MU-MIMO)的资源分配和用户调度,提出了一种基于遗传算法(GA)的多用户比例公平调度算法,并通过在初始群体中加入具有优秀基因的个体并保持其基因稳定性,加快了遗传算法的收敛性。基站端采用块对角化预编码机制来消除用户间干扰,并在保证用户间速率比例公平的前提下最大化系统吞吐量,在同一时刻选择最优用户集来传输。此外,分析了信道反馈错误对该系统的影响。仿真与分析表明,提出的基本算法与改进算法在公平性和系统的吞吐量方面取得了较好的折中,同时算法复杂度比较低。     

一种低复杂度非正交多址接入功率分配算法

功率分配是非正交多址系统(NOMA)资源分配中的一个重要研究问题。最优迭代注水功率分配算法能提高系统性能,但是算法复杂度较高。提出一种低复杂度的功率分配算法,首先对子载波采用注水原理得到总的复用功率,然后在单个子载波上叠加用户间采用分数阶功率分配方法进行功率再分配。通过仿真分析,与最优迭代注水功率分配算法相比,该算法在性能损失不超过3%的情况下,大幅减低了计算复杂度。     

基于格基缩减的MU-MIMO下行传输策略

块对角化（Block diagonalization, BD）预编码算法通过两次奇异值分解实现多用户间干扰消除并将下行多用户多输入多输出（Multi-user multiple-input multiple-output, MU-MIMO）信道解耦成多个独立 的单用户MIMO（Single-user MIMO, SU-MIMO）信道,但其计算复杂度也随着用户数量和矩阵维数的增加而增加 。在MU-MIMO下行系统中,提出基于格基缩减的改进块对角化传输策略,将BD算法的第2次奇异值分解替换为基于格基缩减的线性检测,可得到比传统BD传输策略更好的误码率性能以及更低的计算复杂度。     

基于信干噪比反馈的多基站协作预编码

针对蜂窝小区的干扰,给出了基于信干噪比(SINR)反馈的基站协作策略,采用SINR门限锁定系统边缘用户,避免了传统穷搜索算法的复杂性,在多小区规模上索引协作基站。基于SWF功率分配,采用SVD预编码设计。仿真表明,该方案既弱化了小区边缘用户受到的干扰,提高了系统信息吞吐量,又减少了高反馈量带来的复杂度,是一个很好的折中方案。     

分布式系统中的多用户并行调度算法

针对传统调度算法的不足,在多用户分布式天线系统的下行链路中引入并行调度思想,从而在系统吞吐量和公平性能间取得折中。首先将轮询调度算法进行了改进,同时提出一种并行调度算法,每次选择瞬时数据率和平均吞吐量较高的多个用户进行通信。采用优化的匹配算法改进传统穷尽搜索计算量大的缺陷。仿真结果显示与传统的并行调度和改进的并行轮询调度相比,本文算法在保证吞吐量的情况下,公平性有了显著提高。     

下行NOMA系统中一种考虑用户QoS的功率分配方案

为了提高未来网络的系统吞吐量和频谱效率,非正交多址接入技术(non-orthogonal multiple access,NOMA)的功率分配问题成为了近年来移动通信领域研究重点。在保证用户服务质量的前提下,针对下行NOMA系统的功率分配问题进行了研究,在子带间采用迭代注水算法,小范围注水分配各子带的功率,在子带内考虑用户服务质量,利用卡罗需—库恩—塔克(Karush-Kuhn-Tucker,KKT)条件求出最优功率分配因子,以此最大化系统总吞吐量。仿真证明,提出的功率分配方案能达到的系统总吞吐量、频谱效率优于传统的功率分配方案,其系统总吞吐量比正交多址接入技术(orthogonal multiple access,OMA)提升了约40%以上。     

一种改进的PU2RC多模传输方法

在有限反馈的下行多用户多入多出(Multiuser multiple-input multiple-output,MU-MIMO)系统中,系统的和容量随着信噪比的升高而干扰受限。本文针对这个问题提出了一种改进的每用户归一化波束成形与速率控制(Per-user unitary beamforming and rate control,PU2RC)多模传输方案,通过增加信道量化质量指示的反馈,为基站进行用户选择提供了更有效的信息以实现多模传输。仿真结果表明,在实际系统中激活用户数比较少时,该方案能够通过动态改变同时服务的用户数,从而最大化系统的和容量。在同等反馈量的情况下,与传统的PU2RC相比,提高了系统的和容量,为下行MU-MIMO系统的应用提供了更好的参考。     

基于模代数预编码的MIMO室内可见光通信系统

针对基于模代数预编码(THP)的多用户多输入多输出(MIMO)室内可见光通信系统存在误码率高、功率利用率低的问题,提出一种基于格基规约THP的多用户MIMO(MU-MIMO)通信系统。通过改进的格基规约THP对信道矩阵进行优化使其满秩,在此基础上,设计多用户接收信号均衡分配方案。仿真结果表明,在信噪比较高且信道不稳定的情况下,MU-MIMO通信系统具有较强的鲁棒性以及较低的误码率,多用户接收信号均衡分配方案有效解决了THP算法中用户接收信息的公平分配问题。     

一种多用户OFDM系统的资源调度算法

正交频分复用(OFDM)系统中,资源调度是影响整个网络性能的关键问题,而现有的静态资源调度算法和动态资源调度算法不能很好地兼顾系统吞吐量以及用户公平性两发面的性能.在分析现有算法的性能特点的基础上,基于比例公平准则,提出了一种多用户OFDM系统下行链路中的动态调度算法,并进行了数值仿真.仿真结果表明,在基站发射功率及用户吞吐量公平分配的约束下,系统吞吐量性能较好.且算法在用户吞吐量公平性上有所提高,复杂度较低.     

认知超密集网络用户关联与资源分配联合优化遗传算法

针对下行的异构认知超密集异构网络（UDN）的多维资源配置问题,提出一种以毫微微小区用户最大吞吐量为目标的联合优化用户关联和资源分配的改进遗传算法。首先,在算法开始之前进行预处理,初始化用户可达基站和可用信道矩阵;其次,采用符号编码,将用户与基站以及用户与信道的匹配关系编码为一个二维的染色体;然后,将动态择优复制+轮盘赌作为选择算法,以加快种群的收敛;最后,为避免算法陷入局部最优,在变异阶段加入早熟判决的变异算子,从而在有限次迭代下求得基站、用户、信道的连接策略。实验结果表明,在基站与信道数量一定时,所提算法与三维匹配的遗传算法相比在用户总吞吐量方面提高了7.2%,在认知用户吞吐量方面提高了1.2%,且计算复杂度更低。所提算法缩小了可行解的搜索空间,能在较低复杂度下有效提高认知UDN的总吞吐量。     

Efficient User Pairing for Performance Enhancement of Downlink NOMA Systems

In this paper, the resource allocation problem for user pairing (UP) in downlink non-orthogonal multiple access (NOMA) systems is investigated. NOMA allows the use of one subcarrier for more than one user at the same time, thus increases the total capacity of the wireless communication system. However, users pairing is a challenging task in the NOMA systems, because a good channel quality subcarrier should be selected and allocated for the user to enhance the performance of NOMA systems. The proposed UP algorithm aims to enhance the sum rate of the paired users per subcarrier and consequently enhance the total sum rate of downlink NOMA systems. Moreover, the proposed UP algorithm target to improve the fairness of the users. The proposed UP algorithm is based on a simple search for the subcarrier with the minimum average channel gains to be assigned its paired users and then excluding it from the next searching process. The proposed scheme ensures the higher channel gain for users by giving the priority to the subcarrier with the minimum average channel gains during the user pairing process. The simulation results demonstrate that the proposed UP algorithm can not only enhance the total sum rate compared with the random UP and conventional UP but also can enhance the fairness of the users. Moreover, it is clearly seen that the proposed UP algorithm provides the lowest outage probability.     

A utility-based algorithm for joint uplink/downlink scheduling in wireless cellular networks

The major driver for deploying next generation wireless cellular systems is their ability to efficiently deliver resource-demanding services, many of which require symmetric communication between an uplink mobile user and a downlink mobile user that belong to the same network. In this work, we propose a utility-based joint uplink/downlink scheduling algorithm suitable for wireless services involving pairwise communication among mobile users. While most existing literature focuses on downlink-only or uplink-only scheduling algorithms, the proposed algorithm aims at ensuring a utility function that jointly captures the quality of service in terms of delay and channel quality on both links. By jointly considering the time varying channel conditions in both the uplink and the downlink directions, the proposed algorithm avoids wasting of resources and achieves notable performance gains in terms of increased number of active connections, lower packet drop rate, and increased network throughput. These gains are achieved with a tradeoff cost in terms of complexity and signaling overhead. For overhead reduction in practical scenarios, we propose an implementation over clusters within the network.     

一种新的多用户波束赋形算法

为了抑制多用户多输入多输出(Multi-user multiple input multiple output,MU-MIMO)系统中的用户间干扰,提出一种新的算法。在本用户消除对其他用户的干扰的前提下选择波束赋形矢量,如果所有用户都采取这种措施,各个用户受到其他用户的干扰将降低至最低,从而提高整个系统的性能。仿真结果表明,随着用户数增加,采用本文中的算法较传统的信漏噪比(Signal-to-leakage-and-noise ratio,SLNR)算法可以降低系统误码率(Bit error rate,BER)。     

基于NOMA系统的用户分组和功率分配策略

针对非正交多址接入（NOMA）系统的最优用户分组和功率分配方案的高复杂度问题,提出一种改进的用户分组和功率分配策略。首先,对用户进行分组,由信道增益值确定每个子信道的第一个用户,再由贪婪匹配方法分配剩余用户;然后,对用户的功率进行分配,将该功率分配问题分成子信道间和子信道内两部分,对于子信道间采用线性注水算法分配功率,对于子信道内则采用所提的迭代功率分配算法分配功率;最后,构造拉格朗日函数以实现在最大发射功率以及保证每个用户最低数据速率的约束下系统吞吐量的最大化。仿真结果表明,在多用户的情况下,与线性注水的分数阶功率分配（LWF-FTPA）算法和等分数阶功率分配（EQ-FTPA）算法相比,系统吞吐量分别提高了8%和20%,所提策略优于传统算法。     

非理想条件下OFDMA系统物理层安全的资源分配算法

针对OFDMA系统中用户QoS需求的差异性,提出了一种非理想状态下不同用户的资源分配算法,即求取用户携带比特数和发射功率最优解问题。基于自适应功率分配增益较小的情况,通过进行功率的平均分配来降低算法复杂度,基于吞吐量最大化原则,将剩余未分配的子载波分配给能获得最大传输速率的用户,提升系统的整体吞吐量水平。仿真结果表明:相对于其他传统资源分配算法,这个算法能够保证不同混合用户的最小传输速率要求的同时,有效提升算法的公平性。     

A computationally efficient genetic algorithm for MIMO broadcast scheduling

In conventional single-input single-output (SISO) systems, the capacity is limited as base station can provide service to only one user at any instant. However, multiuser (MU) multiple-input multiple-output (MIMO) systems deliver optimum system capacity by providing service to multiple users (as many as transmit antennas) simultaneously according to dirty paper coding (DPC) scheme. However, DPC is an exhaustive search algorithm (ESA) where the user encoding sequence is important to transmit data to multiple users. Exhaustive search becomes imperative as the search space grows with number of users and number of transmit antennas in the MU MIMO system. This can be treated as an optimization problem of maximizing the achievable system sum-rate. In this paper, it has been demonstrated that combined user and antenna scheduling (CUAS) with binary genetic algorithm (BGA) adopting elitism and adaptive mutation (AM) achieves about 97–99% of system sum-rate obtained by ESA (DPC) with significantly reduced computational and time complexity. It has been shown that BGA is able to find the globally optimum solution for MU MIMO systems well within the time interval of modern wireless packet data communications. However, it is interesting to observe that BGA is able to find a solution to CUAS close to the optimum value quite rapidly. In this paper, it is also shown that BGA with elitism and AM achieves higher throughput than limited feedback scheduling schemes as well.     

A joint antenna and user selection scheme for multiuser MIMO system

In this paper, we present a low-complexity algorithm for real-time joint user scheduling and receive antenna selection (JUSRAS) in multiuser MIMO systems. The computational complexity of exhaustive search for JUSRAS problem grows exponentially with the number of users and receives antennas. We apply binary particle swarm optimization (BPSO) to the joint user scheduling and receive antenna selection problem. In addition to applying the conventional BPSO to JUSRAS, we also present a specific improvement to this population-based heuristic algorithm; namely, we feed cyclically shifted initial population, so that the number of iterations until reaching an acceptable solution is reduced. The proposed BPSO for JUSRAS problem has a low computational complexity, and its effectiveness is verified through simulation results.     

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。