Weighted Gauss-Seidel Precoder for Downlink Massive MIMO Systems

In this paper,a novel precoding scheme based on the Gauss-Seidel(GS)method is proposed for downlink massive multiple-input multiple-output(MIMO)systems.The GS method iteratively approximates the matrix inversion and reduces the overall complexity of the precoding process.In addition,the GS method shows a fast convergence rate to the Zero-forcing(ZF)method that requires an exact invertible matrix.However,to satisfy demanded error performance and converge to the error performance of the ZF method in the practical condition such as spatially correlated channels,more iterations are necessary for the GS method and increase the overall complexity.For efficient approximation with fewer iterations,this paper proposes a weighted GS(WGS)method to improve the approximation accuracy of the GS method.The optimal weights that accelerate the convergence rate by improved accuracy are computed by the least square(LS)method.After the computation of weights,the different weights are applied for each iteration of the GS method.In addition,an efficient method of weight computation is proposed to reduce the complexity of the LS method.The simulation results show that bit error rate(BER)performance for the proposed scheme with fewer iterations is better than the GS method in spatially correlated channels.

An Enhanced Jacobi Precoder for Downlink Massive MIMO Systems

Linear precoding methods such as zero-forcing(ZF)are near optimal for downlink massive multi-user multiple input multiple output(MIMO)systems due to their asymptotic channel property.However,as the number of users increases,the computational complexity of obtaining the inverse matrix of the gram matrix increases.Forsolving the computational complexity problem,this paper proposes an improved Jacobi(JC)-based precoder to improve error performance of the conventional JC in the downlink massive MIMO systems.The conventional JC was studied for solving the high computational complexity of the ZF algorithm and was able to achieve parallel implementation.However,the conventional JC has poor error performance when the number of users increases,which means that the diagonal dominance component of the gram matrix is reduced.In this paper,the preconditioning method is proposed to improve the error performance.Before executing the JC,the condition number of the linear equation and spectrum radius of the iteration matrix are reduced by multiplying the preconditioning matrix of the linear equation.To further reduce the condition number of the linear equation,this paper proposes a polynomial expansion precondition matrix that supplements diagonal components.The results show that the proposed method provides better performance than other iterative methods and has similar performance to the ZF.