Efficient Successive Over Relaxation Detectors for Massive MIMO

Abstract

Massive multiple-input and multiple-output (MIMO) is considered as an advanced technique in wireless communication due to its high spectral efficiency, data rates, and transmission reliability. However, the large number of antennas makes it challenging to design efficient detectors since computational complexity is much higher. Thus, conventional linear methods like minimum mean square error (MMSE) detection suffer from prohibitive complexity. To balance complexity and performance, a class of iterative linear methods are considered to approach MMSE performance with $\mathcal {O}({N_{T}}^{2})$ complexity, where $N_{T}$ is the number of transmit antennas. Among them, successive over relaxation (SOR) detector is promising since it achieves near MMSE performance with affordable complexity and fast convergence. However, error performance of previously proposed SOR detectors is sensitive to a relaxation factor $\omega $ , especially when the channel varies. To address this issue, two detectors are proposed in this paper: non-adaptive SOR (NA-SOR) detector (with fixed $\omega $ ) and adaptive SOR (A-SOR) detector (with iteratively changed $\omega $ ). Compared to prior SOR detectors, the performance of the NA-SOR detector is more robust to $\omega $ , and the A-SOR detector achieves better performance, especially in highly correlated channels. An efficient pipelined hardware architecture of the A-SOR detector is proposed. Implementation results show advantages when compared to the state-of-the-art (SOA).