Research On Joint Detection And Decoding Algorithm For Massive MIMO System With Polarization Code

With the explosion of mobile data traffic,there is an urgent need to study 5th Generation(5G)technology because the existing mobile communications system can not meet future demand.With the in-depth study of 5G technology,in order to ensure the reliability of information transmission in 5G systems,the 3rd Generation Partnership Project(3GPP)proposes the polar code as the standard specification of 5G,which is one of the channel codes that can approach the shannon limit.Simultaneously,the massive multi-input and multi-output(MIMO),as one of the core technologies of 5G,can effectively improve the spectrum efficiency and power consumption efficiency of the system.Therefore,this thesis focuses on the research on the low-complexity joint detection and decoding algorithms in polarization-coded massive MIMO systems.The specific contents and innovations of this thesis are as follows:1.The summary of classic low complexity signal detection algorithm and polar code decoding algorithm of the massive MIMO system.Based on the introduction to the massive MIMO systems,the detection performance and complexity of the four low-complexity signal detection algorithms for the massive MIMO system,including Neumann series expansion method,Jacobi method,Gauss-seidel method and Successive Over Relaxation(SOR)method,are assumarized and comparied.Besides,the polar codes and the corresponding SC decoding algorithms,are analyzed with different code lengths.2.Low-complexity signal detection algorithm based on Transpose-Free Quasi-Minimal Residual combined with polar code in the massive MIMO system.In order to avoid the problem of high-order matrix inversion and decrease the computational complexity from O(N_t~3) to O(N_t~3),where N_t denotes the total number of users' antennas in the system,we tranform the problem of signal detection into the problem of linear equations and use the Transpose-Free Quasi-Minimal Residual(TFQMR)method to solve a set of linear equations in an iterative manner to obatin the estimate of the transmitted signal.Simulation results show that the TFQMR detection algorithm in the massive MIMO system combined with polar code,compared with the Neumann series expansion method,has obvious advantages over both the complexity and performance.Additionally,it can achieve close the minimum mean square error(MMSE)performance.3.The iterative detection and decoding algorithm based on symmetric LQ combined for massive MIMO with polar code.A low complexity symmetric LQ(S-LQ)signal detection algorithm is proposed in the massive MIMO combined with polar code.In this method,the multi-user signal detection problem is converted into a linear system of equations and iteratively solves it,whose solution vector is used as an estimate of signal vector.Simulation results show that the S-LQ-based detection method is superior over the Neumann series expansion method in performance and complexity.The S-LQ-based detection method is superior over the TFQMR method in complexity,but the S-LQ-based detection method is slight worse than the TFQMR method in performance.Therefore,to further improve the performance of the massive MIMO combined with polar code's system,the iterative detection and decoding algorithm based on S-LQ detection with SC decoding algorithm is taken into consideration together.Simulation results show that the iterative detection and decoding algorithm based on S-LQ is superior to the iterative detection algorithm based on S-LQ in performance.