Signal Sub-Space Based Variable Step Size LMS Adaptive Beamforming Algorithm for Massive MIMO Systems

Abstract

In this paper, an improved variable step size LMS adaptive beamforming algorithm based on signal sub-space (SS-VSLMS) is proposed to solve some problems of LMS adaptive beamforming algorithm when the eigenvalue of the correlation matrix is not ideal for large scale MIMO systems. The weight vector in the algorithm only preserves the subspace component of the signal, and can obtain the optimal step length based on the exponential factor and improve the convergence speed of the algorithm. At the same time, the error correlation analysis is introduced to minimize the steady state error, and the power vector of the signal subspace is extracted to improve the anti-interference ability of the system. Simulation results show that the improved algorithm can meet the convergence and anti-interference requirements of large-scale MIMO systems.