Research On Large-scale MIMO Channel Recovery Technology Based On Compressed Sensing Theory

The rapid development of wireless mobile communication technology has made large-scale multiple-input-multiple-output(MIMO)technology an important direction for the development of Internet mobile communication technology in the 21 st century.As the number of antennas configured at the base station and the user side in massive MIMO continues to increase,the throughput of the system also increases,making it more and more difficult to obtain channel state information(CSI).In order to improve the performance and stability of the massive MIMO system,thereby promoting the rapid development and innovation of wireless mobile communication technology,the channel recovery algorithm of the massive MIMO system has gradually attracted the attention of researchers,and it has become a hot research field.This article is mainly based on Compressive Sensing(CS)technology to carry out in-depth research on the channel recovery algorithm of massive MIMO system.The research work is as follows:Firstly,the reconstruction algorithm related to compressed sensing is introduced,the basic principle and basic flow of the algorithm are analyzed in detail,and simulation is performed.The analyzed data results pave the way for the next in-depth research.Secondly,the feasibility of applying Semi-IHT algorithm to massive MIMO channel recovery is studied.This work first introduces the channel model of the massive MIMO system in detail,and analyzes the pain points that are difficult to solve in the current channel estimation algorithm,and then proposes the problem to be solved in this article,and then converts the rank minimization problem into matrix completeness.For this problem,a semi-iterative hard threshold algorithm is proposed.The algorithm is based on the compressed sensing IHT algorithm.The information matrix is obtained by modifying the search direction of the iterative hard threshold reconstruction algorithm and adaptively adjusting the iteration step length.Simulation results show that the performance of this algorithm is better than traditional methods.Finally,this paper focuses on a variable-step rule backtracking SAMP algorithm based on compressed sensing theory,and uses it for channel estimation of massive MIMO systems.The specific operation is to first select a fixed step size in the SAMP algorithm for the least square method,and then optimize the selection process on this basis.Through this method,the signal sparsity can be approximated faster.At the same time,the residual value is used for selection in the initial stage to reduce the number of iterations of the algorithm.Then use the idea of regularization to perform secondary screening on the selected atoms,optimize the step size selection of the SAMP algorithm,and automatically adjust the step size to estimate the channel.In the end,through simulation and comparison,it is proved that the algorithm not only improves the reconstruction accuracy of the traditional SAMP algorithm in channel estimation,but also has a good application prospect.