ML Detection in Phase Noise Impaired SIMO Channels with Uplink Training

TL;DR

This paper derives optimal maximum likelihood detectors for SIMO systems affected by phase noise, analyzing their performance in various scenarios and showing how antenna number influences error floors.

Contribution

It provides the first comprehensive derivation of ML detectors for phase noise impaired SIMO channels under different noise source correlations and channel conditions.

Findings

SER floors are present in all scenarios at high SNR.

The SER floor is independent of antenna count with identical phase noise sources.

Increasing antennas reduces the SER floor with independent phase noise sources.

Abstract

The problem of maximum likelihood (ML) detection in training-assisted single-input multiple-output (SIMO) systems with phase noise impairments is studied for two different scenarios, i.e. the case when the channel is deterministic and known (constant channel) and the case when the channel is stochastic and unknown (fading channel). Further, two different operations with respect to the phase noise sources are considered, namely, the case of identical phase noise sources and the case of independent phase noise sources over the antennas. In all scenarios the optimal detector is derived for a very general parametrization of the phase noise distribution. Further, a high signal-to-noise-ratio (SNR) analysis is performed to show that symbol-error-rate (SER) floors appear in all cases. The SER floor in the case of identical phase noise sources (for both constant and fading channels) is independent of the number of antenna elements. In contrast, the SER floor in the case of independent phase noise sources is reduced when increasing the number of antenna elements (for both constant and fading channels). Finally, the system model is extended to multiple data channel uses and it is shown that the conclusions are valid for these setups, as well.