大规模MIMO系统基于分布式压缩感知的信号检测算法研究

随着通信技术的不断发展,人们对通信速率的要求越来越高,大规模MIMO(Multiple-Input Multiple-Output)技术因其能够大大提高系统的频谱效率,成为通信技术领域的研究热点.在大规模空间调制MIMO中,原本最佳的检测方法——最大似然(Maximum likelihood,ML)检测算法由于算法复杂度过高,不再适用.而适用于小规模空间调制MIMO系统的低复杂度的检测算法在大规模系统中性能会很差.本文利用空间调制信号的结构化稀疏性,提出了基于分布式压缩感知(Distributed Compressed Sensing,DCS)的信号检测算法,同时参照已有文献,利用分组传输和信号交织来进一步提高信号检测性能.最后我们通过仿真验证了此方案能够较好地逼近最大似然检测算法性能.

A Signal detection algorithm based on distributed compressed sensing in Massive MIMO system

With the continuous development of communication technology,people have higher requirements for communication speed.Large-scale MIMO(Multiple-Input Multiple-Output)technology has become a hotspot in the field of communication technology because it can greatly improve the spectral efficiency of the system.In the large-scale space modulation MIMO,the o-riginal best detection method -Maximum Likelihood(ML)detection algorithm is no longer applicable because of the complexity of the algorithm.And low-complexity detection algorithms for small-scale space-modulated MIMO systems can perform poorly in large-scale systems.In this paper,the signal detection algorithm based on Distributed Compressed Sensing(DCS)is proposed by using the sparseness of spatial modulation signal.At the same time,the signal detection performance is improved by using packet transmission and signal interleaving.Finally,we verify that this scheme can approximate the performance of maximum likelihood detection algorithm.