An Efficient and Fast-convergent Detector for 5G and Beyond Massive MIMO Systems

Abstract

Massive MIMO (multiple-input multiple-output) is a sub-6GHz wireless access technology that can provide high spectral and energy efficiency and is considered as one of the key enabling technology for 5G, 6G, and beyond networks. The user signal detection during the uplink is one of the major challenges in massive MIMO systems due to the large number of antennas working together at both the user terminal and the base station. The current iterative methods do not offer great efficiency, and the conventional matrix inversion methods are computationally complex due to the large antennas involved in massive MIMO systems. In this paper, we propose a fast and efficient preconditioned iterative method by introducing a preconditioner based on ICF (Incomplete Cholesky Factorization). Additionally, we introduce a novel matrix initializer to further improve the convergence of the proposed algorithm. The numerical results, when compared to conventional methods, show that the proposed algorithm provides better error performance with optimal computational complexity.