Accelerated and Preconditioned Refinement of Gauss-Seidel Method for Uplink Signal Detection in 5G Massive MIMO Systems

Abstract

Massive MIMO (Multiple Input Multiple Output) is a sub-6 GHz wireless access technology, and it is one of the key enabling technologies for the next-generation wireless networks. Massive MIMO groups together antennas at both transmitter and the receiver and achieves high spectral and energy efficiency. Although massive MIMO offers immense benefits, it has to overcome some implementation challenges to make this system a reality. One of the fundamental issues in the massive MIMO system is the uplink signal detection, which becomes inefficient and computationally complex with a large number of antennas. In this paper, we propose an accelerated and preconditioned refinement of the Gauss-Seidel method for uplink signal detection in massive MIMO systems. We further increase the convergence rate of the proposed algorithm by applying Jacobi preconditioner and introducing a novel matrix initialization scheme. The simulations results show that the proposed algorithm achieves near-optimal Bit Error Rate (BER) performance, and it is computationally efficient, compared to conventional detection algorithms. Additionally, we also propose a novel hardware architecture for our proposed algorithm, which helps to identify the required physical components and their interrelationships.