Research On Signal Detection Algorithms For Massive MIMO System

As one of the key technologies of future wireless communication systems,massive multiple input multiple output(MIMO)technology has received extensive attention in recent years.The deployment of large-scale antennas has potential multiplex gain and diversity gain,which greatly improves the spectral efficiency and energy efficiency of the system.However,the complexity of the system uplink signal detection has become a new problem to be solved urgently.In massive MIMO systems,the linear minimum mean square error detection(MMSE)algorithm can achieve near-optimal performance.However,because the MMSE method involves large-scale matrix inversion operation,it still faces the problem of excessive complexity.There are already Neumann series expansion(NSE)methods or iterative methods such as Jacobi to solve the problem of high complexity of matrix inversion,but the computational complexity of the matrix multiplication or the initialization part of iterative method is still very considerable.Therefore,this paper aims to propose low complexity signal detection algorithms for uplink massive MIMO systems.Firstly,this paper briefly introduces the massive MIMO system model and its properties,as well as the structure and convergence of common iterative algorithms.The complexity of different iterative algorithms is briefly analyzed and the performance of the algorithm is simulated and compared.The limitations of the existing "best" relaxation factor in the actual massive MIMO system are analyzed and improved.According to the simulation results,the limitations of the existing "optimal" relaxation parameters of successive overrelaxation method in the actual massive MIMO system are analyzed and improved.The relaxation methods using the improved relaxation parameter have higher convergence probability,wider application range and better performance.Then an iterative algorithm based on block matrix is constructed,which is simple and low complexity.Then the steepest descent method is utilized to provide the fastest descent direction for the proposed block iteration algorithm,and the joint detection algorithm is obtained.The convergence of the proposed joint detection algorithm is proved and the complexity is analyzed.Finally,the simulation results show that the proposed joint detection algorithm has better detection performance while maintaining the same level of complexity as common detection algorithms.And the superiority is more obvious when the ratio of the number of base station antennas to the number of users is low.Finally,the mathematical relationship between the two main detection methods,the approximation methods and the iterative methods,is deduced and verified by simulation.Based on the obtained relationship,an improved Newton iterative algorithm is proposed,and the superiority of its performance is verified by simulation.Then the complexity of the initialization part is optimized as the main complexity.In order to further reduce the complexity of the algorithm,an NRI hybrid iterative algorithm is proposed.The complexity analysis and simulation results show that the proposed NRI algorithm has obvious advantages in both complexity and performance,and is suitable for massive MIMO systems.It is worth mentioning that the simulation results show that the proposed algorithm is still robust when the channel state information(CSI)is not perfect.