Low-complexity Detection Algorithms For Massive MIMO Systems

MIMO systems with many antennas at both receiver and transmitter sides have some advantages in terms of data rate and transmit diversity, so they have become very popular and have been mature in study. In order to improve channel capacity and spectral efficiency, researchers consider massive MIMO systems which use a large-scale array antennas. Studies of massive MIMO systems are still at an early stage until now. Since detector at the receiver is the important component of MIMO systems, studying the detection algorithms will promote the development of researches in massive MIMO systems greatly.Detection algorithms of conventional MIMO systems including ZF, MF, MMSE, ZF-SIC and so on. Sphere decoding algorithm is a representative detection algorithm of conventional MIMO systems, it can achieve near-ML performance at the cost of high computation complexity. The number of antennas of Massive MIMO systems in this paper are in the order of tens to hundreds, conventional detection algorithms in the systems act poorly and their detection complexities are particularly unbearable. So it is very important to looking for some low-complexity detection algorithms which are suitable for massive MIMO systems.In this paper, we first of all introduce the research background and the research status about massive MIMO technologies, pointing the study direction simultaneously. Then we simply introduce the basic information of massive MIMO systems. This part mainly analyzes the sum rate upper bound issue, and explores some key technologies in massive MIMO systems, including channel estimation technology, downlink precoding method and uplink detection algorithm. In terms of research status and technical characteristics,we introduce the technologies mentioned above. We also present some difficulties and hot issues of massive MIMO systems which are still required to be studies further. This paper focus on the research of low-complexity detection algorithms of massive MIMO systems. In this part, we firstly introduce LAS detector in detail. The LAS detector is based on local neighborhood search. We have put in large text to explain the fundamental principle and the implementation procedure of the LAS detection algorithm. Then the analysis of computation complexity generated from the LAS algorithm is made. Also verifications of the detection performance in LAS via simulating are made. Based on the LAS detection algorithm, the MOS-LAS algorithm has been proposed. MOS-LAS improve the detection performance of the LAS detector by means of generating some local minimum points and selecting the best point. According to the different ways of achieving the aim above, MOS-LAS has been subdivided into MIV-LAS algorithm and MSCS-LAS algorithm. We introduce the idea of these algorithms in detail,including MIV-LAS and MSCS-LAS. And also verify the effectiveness of these algorithms by simulating. At last we introduce another low-complexity RTS detector. We describe the fundamental principle and the implementation procedure of RTS, then make a comparison with RTS and LAS roundly. Finally verifications of the detection performance in RTS are made. We can draw the conclusion that the BER performance of the RTS algorithm has overmatch the LAS algorithm from the simulation results.