Robust Precoding Downlink Transmission For Massive MIMO Systems

With the development of mobile communication technology,mobile intelligent terminal has gradually become an indispensable part of people's daily life.Massive multi-input-multi-output(MIMO)is considered to be one of the most important technologies in the next generation of mobile communication systems due to the effective increase in spectrum efficiency.This paper surrounding the massive MIMO systems in mobile environment downlink transmission performance problems,from how to improve base station side channel state information(CSI),and how to under the condition of imperfect CSI precoding design of these two aspects study,studies the robust precoding method utilizing both statistical and instantaneous channel information.Firstly,the classic linear precoding methods are reviewed,including matched filter(MF),zero-force(ZF)and regularized zero-force(RZF),so as their sum-rate performance are compared.In order to solve the problem that it is difficult for the base station to obtain the perfect CSI in the practical massive MIMO systems,the robust precoding method with maximum sum-rate is reviewed by utilizing the massive MIMO channel posteriori model which considers both instantaneous channel information and statistical channel information.Simulation results show that the performance of three typical linear precoding methods decreases significantly with the increase of sounding period when perfect CSI cannot be obtained.Meanwhile,robust precoding methods can maintain high performance.Secondly,starting from the optimization problem equivalent to the ergodic sum-rate maximization optimization problem in the existing robust precoding methods,a machine-learning-assisted robust precoding method is designed.The computation modules in the robust precoding method are optimized by means of machine learning and coefficient weighting.The simulation results show that,compared with the existing methods,the approximate optimal machine-learning-assisted robust precoding method can effectively reduce the computational complexity of the systems and at the same time approximate the sum rate performance of the existing robust precoding method.Thirdly,a machine-learning-assisted robust precoding method with low complexity is proposed.The precoding matrix in this method is weighted by the improved beam selection method precoding matrix and RZF precoding matrix.The weighting coefficient is obtained by machine learning method.The simulation results show that the complexity of the low-complexity machine learning-assisted robust precoding approach is close to that of the beam selection method,and it can be utilized in both quasi-static and fast channel environments.Finally,four kinds of channel representation,channel prediction methods based on linear extrapolation and auto-regressive model(AR,Autoregressive)are studied.Furthermore,channel prediction method is utilized to assist various precoding methods,including RZF precoding method and robust precoding method.The simulation results show that the channel prediction method based on AR and beam-latency domain channel matrix can obtain the lowest channel prediction error and improve the sum-rate performance of RZF precoding and robust precoding in the mobile scenario effectively.