Research On Signal Detection Technology For Uplink Massive MIMO

In wireless communication,the number of antennas at the base station in the Multiple-Input Multiple-Output(MIMO)technology is expanded to hundreds and provides services for dozens of users at the same time,which is called massive MIMO technology.Massive MIMO technology is known as the key technology of the next generation wireless communication system,which not only improves the spectrum efficiency,but also expands the system capacity.In the uplink system,due to the large number of antennas and radio frequency chains,the complexity of signal detection technology is greatly increased.Therefore,finding a massive MIMO detection scheme with the best performance and lower complexity has important research value.Based on this,the main research contents of this article are as follows:1.In order to solve the problem of high computational inversion complexity caused by large-scale antenna arrays,a Weighted Two-Stage(WTS)iterative algorithm is introduced into the signal detection,and the WTS is processed by hard and soft decisions.The detection is simulated and compared with traditional algorithms.By combining the Steepest Descent(SD)algorithm with the WTS algorithm,a SDWTS soft output detection algorithm is proposed.This algorithm takes advantage of the good search performance of SD algorithm,improves the convergence speed of WTS algorithm,and effectively reduces multi-user interference.Numerical results show that the proposed WTS detection performance is better than over-relaxation iterative detection and Gauss Seidel iterative detection,while SDWTS detection has a small improvement in performance compared to WTS detection.In addition,the best performance can be obtained through a small number of iterations.The WTS algorithm is composed of two half-iterations,and the two half-iterations are merged together by weighting coefficients,thereby reducing the complexity of the algorithm.2.In order to solve the problem that the Richardson Iteration(RI)algorithm requires a large number of iterations to complete the convergence,the Conjugate Gradient(CG)algorithm is introduced to preprocess the RI.Generally,the CG algorithm converges faster than the steepest descent algorithm,and the strong search performance of the CG algorithm provides an effective search direction for the RI algorithm.Therefore,a new CGRI joint detection soft output algorithm is proposed,which further accelerates the convergence speed of the algorithm.At the same time,the CGRI algorithm uses a soft decision method to recover the target signal.In addition,the optimal relaxation parameters are introduced to further accelerate the convergence speed of the algorithm.The simulation results show that the performance of the improved CGRI joint detection algorithm is better than that of the traditional signal detection algorithm.When the signal-to-noise ratio increases,the performance gradually approaches the Minimum Mean Square Error(MMSE)Bit Error Rate(BER)lower bound.