An Approximate ML Detector for MIMO Channels Corrupted by Phase Noise

TL;DR

This paper introduces an efficient approximate ML detection algorithm for MIMO channels affected by phase noise, achieving near-ML performance with low computational complexity, close to that of conventional MIMO detection.

Contribution

The paper proposes the self-interference whitening (SIW) detection algorithm for phase noise-affected MIMO channels, demonstrating near-ML performance with minimal complexity.

Findings

SIW achieves near-ML detection performance in most scenarios.

The algorithm requires only twice the nearest neighbor detection complexity.

Near-ML detection for phase noise MIMO channels is as efficient as for noise-free channels.

Abstract

We consider the multiple-input multiple-output (MIMO) communication channel impaired by phase noises at both the transmitter and receiver. We focus on the maximum likelihood (ML) detection problem for uncoded single-carrier transmission. We derive an approximation of the likelihood function, based on which we propose an efficient detection algorithm. The proposed algorithm, named self-interference whitening (SIW), consists in 1) estimating the self-interference caused by the phase noise perturbation, then 2) whitening the said interference, and finally 3) detecting the transmitted vector. While the exact ML solution is computationally intractable, we construct a simulation-based lower bound on the error probability of ML detection. Leveraging this lower bound, we perform extensive numerical experiments demonstrating that SIW is, in most cases of interest, very close to optimal with moderate phase noise. More importantly and perhaps surprisingly, such near-ML performance can be achieved by applying only twice the nearest neighbor detection algorithm. In this sense, our results reveal a striking fact: near-ML detection of phase noise corrupted MIMO channels can be done as efficiently as for conventional MIMO channels without phase noise.