Research On Low-complexity Precoding Algorithms In Massive MIMO System

With the rapid development of the big data era for mobile Internet,people's demand for data traffic is increasing.The fourth generation(4G)mobile communication system would be unable to meet users' demand for data transmission rate in the near future.Massive MIMO technology is becoming one of the key technologies in the upcoming fifth generation(5G)mobile communication system,with the advantage of further improving the system capacity and spectrum efficiency.The actual Massive MIMO system can achieve many superior performances,such as improving capacity,reliability,efficiency and so on,while it is also facing a series of challenges at the same time.Massive MIMO system deploys a large number of antennas at the base station to serve more users,which will lead to the introduction of large dimension matrix inversion operation in the precoding process,thus greatly increases the computational complexity of the system.Therefore,under the premise of guaranteeing the approximate optimal performance of Massive MIMO,the paper focuses on the low complexity precoding scheme.Firstly,the channel model of wireless communication is introduced in the paper and the channel used in the follow-up study is subject to the Rayleigh fading distribution.The systems model structure of traditional MIMO and Massive MIMO are analyzed in detail,and the simulation and contrastive analysis prove that Massive MIMO can increase the system capacity exponentially.At the same time,the classical linear precoding schemes of Massive MIMO system,such as MF precoding,ZF precoding and RZF precoding,are studied,and its theoretical analysis and performance simulation are also carried out.In order to avoid the direct inverse of the large dimension linear precoding matrix,the precoding algorithm based on Neumann Series Expansion is studied,which is the evaluation criterion for the follow-up research.Secondly,from the angle of reducing complexity,an improved precoding algorithm based on Gauss-Seidel iteration is proposed in the paper.On the basis of the RZF precoding,GS iteration algorithm is used to replace the inverse operation of matrix,and the initial solution vector is optimized for the initial solution vector based on region selection to approximate the transmission signal.The convergence rate and computational complexity are analyzed by theoretical analysis,and compared with classical RZF precoding and NS-based precoding for simulation of system capacity and BER performance.Finally,from two aspects of reducing complexity and improving system performance,an improved precoding algorithm based on Symmetric Successive over Relaxation iteration is proposed in the paper.The algorithm introduces a relaxation factor on the basis of GS,and be treated by symmetric processing,then optimizes the initial solution based on the properties of the channel hardening of Massive MIMO system,and develop an adaptive power control factor update mechanism associated with iteration signal changes.The simple selection method and their necessary values about the optimal relaxation factor and the initial solution are provided.Finally,the complexity of the improved algorithm is analyzed by a theoretical way,and its system capacity and BER performance are compared with the RZF precoding and the GS-based iteration precoding.