莱斯环境下多用户MIMO中继系统研究

发布时间：2018-04-30 05:45

  本文选题：最大比传输 + 放大转发　； 参考：《扬州大学》2017年硕士论文

【摘要】：大规模MIMO(Multiple Input Multiple Output,MIMO)通过在基站配置几十甚至几百根天线,可以在同一信道上服务多个用户,从而将系统容量提升10倍以上且将能源效率提升10-100倍。因此,大规模MIMO被学术界、工业界公认为是5G的关键技术之一。在大规模MIMO系统中,信号传输具有精准的方向,视距信号(Line of Sight,LoS)占据主导地位。莱斯(Ricean)分布能很好地刻画存在LoS信号的信道衰落特性。基于此,考虑Ricean衰落信道,针对多用户MIMO系统,本文展开了一系列研究。首先,针对认知多用户小规模MIMO上行链路(即:N个单天线认知用户在同一信道上和配置M根天线的基站通信),考虑迫零波束成型(zero-forcing transmission,ZF)传输方案,利用非中心Wishart分布等数学知识,本文获得了其在两种功率约束方案下的可达速率的闭合表达式。本文所考虑的两种功率约束条件如下:即(1)单功率约束条件:Ps=Pmax,(2)双功率约束条件:Ps=min(Ia/|gsn,P|2,Pmax),其中Pmax为认知用户n的最大发送功率,Ia为授权用户所能容忍的最大干扰功率,gsn,P为认知用户n到授权用户的信道衰落系数,Ps为认知用户n的发射功率。数值与仿真结果验证了理论分析的正确性。本项研究成果可用于指导MIMO上行链路的设计与优化。接着,针对多用户大规模MIMO双向放大前传中继网络,考虑最大比合并/最大比传输(maximum-ratio combining/maximum-ratio transmission,MRC/MRT),以及 ZF 两种波束成型方案,本文获得了当中继天线数目M →∞时,系统可达速率渐进结果的闭合表达式。特别地,我们考虑了四种典型功率分配方案,即:(1)PU=EU,PR=ER;(2)PU=EU/M,PR=ER;(3)PU=EU,PR=2NER/M;(4)PU = EU/M,PR = 2NER/M。其中PU为用户的传输功率,PR表示中继的总功率,EU,ER是固定值,N表示用户对数。研究表明:在中继天线数目M→∞时,小尺度衰落、共道干扰被消除,可达速率趋向于一个定值。本项研究成果对于大规模MIMO中继网络的设计与性能优化具有重要的指导意义。最后,本文研究了不完备CSI(channel state information,CSI)对多用户大规模MIMO双向放大前传中继网络性能的影响,获得了系统可达速率和能源效率的渐近表达式。研究表明:不完备CSI会降低用户的接收信干燥比,从而降低系统性能;并且,我们发现当中继天线数目M→ ∞时,在已知不完备CSI,并保证一定性能的前提下,用户和中继的发送功率最多都可以降低为原来的1/M。仿真结果与分析结果很好匹配,这部分研究成果为大规模MU-MIMO系统的性能方案的优化与设计提供了很好的理论依据。
[Abstract]:By configuring dozens or even hundreds of antennas in the base station, large scale MIMO(Multiple Input Multiple output Mimo can serve multiple users on the same channel, thus increasing the system capacity by more than 10 times and energy efficiency by 10-100 times. Therefore, large-scale MIMO is recognized as one of the key technologies of 5 G by academia and industry. In large scale MIMO system, the signal transmission has the accurate direction, the line-of-sight signal line of SightLos is dominant. Rice Rican) distribution can well describe the fading characteristics of the channel with LoS signal. Considering the Ricean fading channel, a series of research on multi-user MIMO system is carried out in this paper. First of all, considering the zero-forcing beamforming zero-forcing transmission scheme for cognitive multi-user small-scale MIMO uplink (that is, one of N single-antenna cognitive users on the same channel and base station with M-antenna), using non-central Wishart distribution and other mathematical knowledge. In this paper, we obtain the closed expression of the reachable rate under two kinds of power constraint schemes. The two kinds of power constraints considered in this paper are as follows: 1) single power constraint: 1) single power constraint: Psn Pmax2) 2) double power constraint: Psminn Ia/ GsnN P2Pmax1, where Pmax is the maximum transmit power of the cognitive user n and Ia is the maximum interference power that the authorized user can tolerate, and the GsnP is the maximum interference power that the authorized user can tolerate. The channel fading coefficient of cognitive user n to authorized user is the transmit power of cognitive user n. Numerical and simulation results verify the correctness of the theoretical analysis. This study can be used to guide the design and optimization of MIMO uplink. Then, for the large scale multiuser MIMO bidirectional amplification forward relay network, the maximum ratio combining/maximum-ratio transmission maximum-ratio combining/maximum-ratio transmission MRT scheme and the ZF beamforming scheme are considered. The closed expression of the asymptotic result of system reachability rate. In particular, we consider four typical power distribution schemes, namely, 1 / 1 / 1 / 1 / 1 / 1 / 1 / 2 / 2 / respectively, and / or / or, respectively. Where pu is the transmission power of the user and PR indicates that the total power of the relay is a fixed value N to represent the subscriber logarithm. The results show that when the number of relaying antennas is M ~ 鈭，

本文编号：1823360