A Fast-Convergent Detector Based on Joint Jacobi and Richardson Method for Uplink Massive MIMO Systems

Abstract

Minimum mean squared error (MMSE) detector achieves near-optimal error rate performance for massive multiple-input multiple-output (M-MIMO) systems but involves large-scale matrix inversion operations with high complexity. Therefore, several approximated matrix inversion algorithms have been proposed. However, their convergence turns out to be very slow. In this paper, a new approach based on joint Jacobi and Richardson method is proposed. We show that the proposed method accelerate the convergence rate at low-complexity for different base station (BS)-to-user-antenna ratio (BUAR). Moreover, a promising initial estimate is utilized to achieve closer-to-optimal initialization for the proposed method. To further accelerate the convergence rate, we introduce a new approximated-eigenvalue based relaxation parameter. The convergence proof of the proposed algorithm is also provided in this work. We analyze the computational complexity of different methods and demonstrate the performance differences with numerical simulations.