Research On Detection Method Based On MF-SIC Algorithm In Massive MIMO Systems

Massive multi-input multi-output technology is one of the key technologies in 5G,which has attracted people's attention,and it is one of the important research topics in the field of wireless communication.Compared with the traditional MIMO system,the massive MIMO system is equipped with a large number of transceiver antennas,which is much larger than the MIMO system,to improve the system capacity and spectrum efficiency.Receiver detection technology as an important component of massive MIMO system,the development of massive MIMO is important.Therefore,the thesis studies the detection algorithm of massive MIMO signal to explore the suitable signal detection algorithm for massive MIMO system.This thesis first summarizes the technical characteristics and the research status at home and abroad of lmassive MIMO,and analyzes the system model of massive MIMO.This thesis introduces the current signal detection algorithm of massive MIMO system.The algorithm of maximum likelihood(ML)detection,linear signal detection algorithm and nonlinear signal detection algorithm are introduced respectively,and the algorithm complexity and detection principle are analyzed.Finally,through the experimental simulation,the performance of each detection algorithm is compared and analyzed.The thesis improves the maximum—combined bit-sequence successive interference cancellation(MRC-OSIC)algorithm.This algorithm optimizes the signal detection and sequencing process in the traditional successive interference cancellation algorithm,and its algorithm complexity is lower than the linear signal detection algorithm ZF.The detection performance can also be approximated by the nonlinear signal detection algorithm ZF-SIC.However,the detection effect is still room for improvement.In this thesis,the MRC-OSIC algorithm is improved by combining the multi-feedback strategy of multi-feedback successive interference cancellation(MF-SIC)algorithm.The idea of multi-feedback is used to judge the estimated signal after quantization,whether it is necessary to carry out multi-feedback processing on' the current detection signal,which can improve the accuracy of the current detection signal and reduce the error propagation.Through the experimental simulation,it can be seen that the improved bit error rate performance of the improved algorithm has been improved.In addition,the thesis improves the improved successive interference cancellation(IMF-SIC)by combining the signal detection and ranking method in the traditional successive interference cancellation algorithm.The main drawback of the MF-SIC algorithm is the inaccuracy of the feedback estimation signal.The IMF-SIC algorithm is an improved algorithm for the shortcomings of MF-SIC algorithm.Therefore,from the point of view of improving the system error performance,we combine the sorting method to improve the IMF-SIC algorithm.The simulation results show that the improved algorithm is better than the original algorithm in the massive MIMO system.