A Study On Detection Algorithm In Massive MIMO System

Massive Multiple-input Multiple-output(MIMO)technology has became one of the key technologies of the fifth generation mobile communication system,because it can significantly improve the spectral efficiency of the system,increase the reliability of the link,improve energy efficiency and have more freedom in space.Because of the channel"hardening"characteristic in the Massive MIMO system,so that can achieve the best detection performance of the detection algorithm.Due to the large number of antennas deployed at the base station,the computational complexity of the system's signal detection has became quite high.Therefore,this paper focuses on studying the detection algorithm with low complexity as well as high performance in Massive MIMO system.First of all,this essay introduces the research status,opportunities and challenges faced by Massive MIMO and lists the common misunderstandings in Massive MIMO.In the second chapter,the detection algorithms of ML,ZF-SIC,MMSE-SIC,ZF and MMSE are described in detail,and under the flat Rayleigh collapse channel model,the simulation analysis of the detection performance of the algorithm is given.In the third chapter of this essay,the channel"hardening"characteristic of Massive MIMO system is analyzed,that is,with the increasing number of receiving antennas,the channel tends to be deterministic and the channel state information is mainly distributed on the main diagonal line,which is also called diagonally dominant.Linear detection algorithms in Massive MIMO systems involve large matrix inversion with high computational complexity,which reduces the complexity mainly through the approximate expansion of the Neumann Series,but the complexity is still high.First of all,we propose an improved scheme for large matrix inversion,to decompose the large matrix into the sum of the diagonal matrix and the hollow matrix,then Neumann series approximation,so that the complexity of the algorithm is reduced fromO(K~3)toO(K~2)with the performance of the system is guaranteed.In the next,a new Neumann series approximation algorithm based on Two-diagonal matrix decomposition is proposed,which decomposes the large matrix into the sum of the hollow matrix and a matrix of Two-diagonal elements centered on the main diagonal,and proposed two diagonal decomposition method to obtain the inverse matrix.To improve computing efficiency,we propose a Neumann approximation algorithm which is suitable for computer parallel processing based on Frobenius matrix decomposition,that is,the large matrix is decomposed into the sum of the hollow matrix and a matrix with a diagonal element and a column of the matrix.The inverse matrix is obtained by using the properties of the Frobenius matrix,and optimized it.The simulation results show that the detection performance of Neumann series approximation detection algorithm based on Two-diagonal matrix decomposition and the detection performance of Neumann series approximation detection algorithm based on Frobenius matrix decomposition are all better than that of Neumann series approximation based on main diagonal matrix decomposition;the optimized Frobenius matrix decomposition algorithm is superior in performance and complexity to the detection algorithm based on Frobenius matrix decomposition.In the fourth chapter applies the Cholesky decomposition and Sherman-Morrison formula to calculate the inverse of large matrix,that is,the CSM detection algorithm.The direct inversion can be achieved and the performance loss caused by the Neumann series approximation in the third chapter can be avoided,and the complexity can also be reduced toO(K~2).Finally,the shortcomings of the proposed detection algorithms in the Massive MIMO system and the open problems to be solved are summarized.