Research Of MIMO Detection Technologies In The Receiver Of Wireless Communication Systems

Multiple Input Multiple Output(MIMO)is one of the most important research area in wireless communication technology.Take advantage of spatial multiplexing and spatial diversity,MIMO can achieve high capacity without additional bandwidth;however,it makes the difficulty of signal detection at the receiving end become higher at the same time.Therefore,the key to design MIMO detection techniques is achieving low complexity and high reliability.Considering the contradiction between performance and complexity,this paper makes further research on the existing MIMO detection techniques to achieve balance.In this paper,classical MIMO detection algorithms are studied at first,including ML,linear detection and successive interference cancellation,and then the simulation result compares the advantages and disadvantages of each method.After that,there are two chapters to research how to improve the algorithms with low complexity and bad performance,and also improve the algorithms with high complexity and optical performance.The main work can be divided into two parts:Firstly,this paper studies the Lattice Reduction aided linear detection and the Lattice Reduction aided successive interference cancellation detection algorithm.Lattice Reduction was proposed to improve the bad performance of ZF,MMSE and SIC,while the attached complexity is quite low.This paper makes an improvement of the LR aided SIC with layered idea.At last several layers with nearest point search detection,small complexity attachment can achieve performance improvement.Secondly,this article makes a research on the K-Best detection algorithm and improves it.K-Best was proposed to improve ML,it reduces complexity with performance loss.This paper also introduces the dynamic K-Best detection algorithm and LR aided K-Best algorithm.In this article,LR aided K-Best is also improved.Tree pruning is used to improve LR aided K-Best with complexity reduction and negligible performance loss.Simulation shows that the improved algorithms either makes the performance improved or reduces computational complexity to achieve a balance of performance and computational complexity.