A Deeply Fused Detection Algorithm Based on Steepest Descent and Non-Stationary Richardson Iteration for Massive MIMO Systems

Abstract

Recently, various iterative methods are investigated to achieve linear minimum mean square error (MMSE) detection accuracy for uplink massive multiple-input multiple-output (MIMO) systems. This letter introduces the non-stationary Richardson (NSR) iteration to achieve fast convergence rate, and reduces its complexity with approximate eigenvalues in massive MIMO system. However, when the system scale grows and channel correlation is considered, the performance of NSR method decays obviously. To improve the robustness, this letter further proposes a deeply fused SDNSR algorithm, which effectively overcomes the weakness of NSR method by fully utilizing the information obtained through the steepest descent (SD) method and NSR method. Moreover, the complexity is significantly reduced by adopting matrix-vector multiplication and reusing intermediate results. Simulation results and complexity analysis exhibit that the SDNSR method achieves superior performance with lower complexity compared to the recently reported works.