Compensation of Phase Noise in Massive-MIMO Uplink Communications Based on Expectation-Maximization Algorithm

TL;DR

This paper introduces an iterative expectation-maximization based receiver for massive MIMO uplink systems that effectively compensates phase noise, improving bit error rate and mean square error performance even with multiple oscillator sources.

Contribution

It proposes a novel EM algorithm-based phase noise compensation method tailored for massive MIMO systems with complex oscillator configurations and channel impairments.

Findings

Significant BER and MSE improvements demonstrated.

Performance comparable to ideal conditions without phase noise.

Outperforms existing state-of-the-art phase noise mitigation methods.

Abstract

Phase noise (PN) is a major disturbance in MIMO systems, where the contribution of different oscillators at the transmitter and the receiver side may degrade the overall performance and offset the gains offered by MIMO techniques. This is even more crucial in the case of massive MIMO, since the number of PN sources may increase considerably. In this work, we propose an iterative receiver based on the application of the expectation-maximization algorithm. We consider a massive MIMO framework with a general association of oscillators to antennas, and include other channel disturbances like imperfect channel state information and Rician block fading. At each receiver iteration, given the information on the transmitted symbols, steepest descent is used to estimate the PN samples, with an optimized adaptive step size and a threshold-based stopping rule. The results obtained for several test cases show how the bit error rate and mean square error can benefit from the proposed phase-detection algorithm, even to the point of reaching the same performance as in the case where no PN is present, offering better results than a state-of-the-art alternative. Further analysis of the results allow to draw some useful trade-offs respecting final performance and consumption of resources.