| Massive multiple-input multiple-output(MIMO)technology is one of the most important technologies in the current wireless communication system.The principle is to arrange several antennas at the base station to receive multiple user information from the user side at the same time.The key is reliable signal detection.However,with the increase in the number of antennas at both the receiving and transmitting ends of the system,the computational complexity of traditional signal detection algorithms has increased significantly,and their convergence speed is slow,making them no longer suitable for practical engineering.Therefore,for large-scale MIMO systems,how to ensure signal detection performance while reducing the computational complexity of the algorithm is a significant issue.To address these issues,this thesis analyzes and studies the signal detection algorithm of massive MIMO system,mainly investigating several linear iterative detection algorithms that can avoid matrix inversion operations and making reasonable innovative improvements on this basis.Firstly,this paper addresses the problem of slow convergence of the Richardson(RI)algorithm and proposes a Jacobi-Richardson iterative algorithm(JARI)based on the idea of initial solution preprocessing,which effectively improves the convergence speed of the algorithm and detection performance.Secondly,an iterative matrix is reconstructed based on the idea of matrix partitioning,and traditional iterative algorithms are optimized using this matrix.A Reconstruction Iteration Preprocessing Richardson(RIP-RI)iterative algorithm using reconstruction iteration preprocessing is proposed.The simulation results show that this preprocessing-based approach effectively improves the convergence speed of the algorithm.In order to further improve the convergence speed of the algorithm and balance the detection performance and computational complexity of the algorithm,this paper proposes a hybrid iterative algorithm based on gradient search(CGRI),which utilizes the feature that gradient search can provide an effective search direction for subsequent iterations.By adjusting the order of multiplication operations between high-dimensional matrices,the computational complexity of the CGRI algorithm is effectively reduced.The simulation results show that under the same conditions,the CGRI algorithm has good detection performance and can be applied to signal detection in large-scale MIMO systems. |
