| As one of the fundamental technologies of fifth-generation(5G)mobile communication,multiple-input multiple-output(MIMO)technology boasts several advantages,including high channel capacity,fast transmission rate,low latency,high reliability,and high spectral efficiency.In massive MIMO,signal detection quality is a crucial performance metric for communication systems.While the conventional minimum mean square error(MMSE)method is a widely adopted means of measuring signal detection quality with low bit error rate(BER),the computational complexity of matrix inversion increases significantly as the number of antennas grows.This thesis focus on reducing the computational complexity of massive MIMO signal detection through the use of iteration-based Richardson and Barzilai-Borwein algorithms.Specifically,this thesis aims to enhance signal detection quality in massive MIMO systems while mitigating the complexity challenges posed by increasing antenna numbers.The main contributions of this work are as follows.(1)To address the challenge of high matrix inversion complexity in the MMSE detection algorithm,this thesis propose an improved Richardson iterative detection algorithm.Our approach incorporates the steepest descent method to preprocess the Richardson iterative algorithm,providing an effective search path,and leverages the whole-correction method to assign weights to different approximate solutions,resulting in superior solutions and faster convergence.Simulations show that our algorithm outperforms the traditional Richardson algorithm in terms of convergence rate,while maintaining its advantages across varying numbers of users and modulation methods.Furthermore,our algorithm achieves the same BER performance as the MMSE algorithm with fewer iterations.(2)To address the high single complexity of the Barzilai-Borwein(BB)iterative algorithm and the insignificant improvement of the BER with the number of iterations,a variable-step cauchy Barzilai-Borwein(CBB)massive MIMO signal detection algorithm is proposed.The CBB algorithm,which combines the BB iterative algorithm and the steepest descent method,is introduced to speed up the convergence speed of the massive MIMO signal detection,and the initial value and the single iteration step are selected reasonably to further improve the convergence of the CBB algorithm.Simulations show that the variable-step CBB algorithm adapts to different number of user scenarios,and the algorithm is significantly better than the BB and CBB algorithms,while the BER performance can be close to that of the MMSE algorithm,solving the problem of high computational complexity of MMSE and achieving a balance between the BER and computational complexity of signal detection.(3)To address the problem of slow convergence of CBB iterative algorithm,this thesis proposes a signal detection method using CBB-delayed iterative over-relaxation.By adopting the principle of delayed iterative over-relaxation,a relaxation factor is introduced to the CBB iterative algorithm,which is used to optimize the solution of the t-th iteration and the(t-2)-th iteration of the CBB algorithm,and the relaxation factor and initial value with better BER performance are selected to accelerate the convergence of the algorithm.Simulation shows that the proposed algorithm can effectively improve the system performance by increasing the complexity and still maintain the advantage at different number of users.At the same time,the complexity is lower compared with the traditional MMSE algorithm,and the approximate optimal performance can be achieved with a small number of iterations. |
