Phase-Aware Code-Aided EM Algorithm for Blind Channel Estimation in PSK-Modulated OFDM

TL;DR

This paper introduces a phase-aware EM algorithm for blind channel estimation in PSK-modulated OFDM systems, effectively resolving phase ambiguity issues by leveraging decoder extrinsic information, leading to improved convergence and reliability.

Contribution

The paper proposes a novel phase-aware EM algorithm that exploits PSK symmetry to resolve phase ambiguity, enhancing blind channel estimation in OFDM systems.

Findings

Reduces local convergence rate from 80% to nearly 0% in simulations.

Successfully resolves phase ambiguity during initialization.

Maintains low complexity during turbo iterations.

Abstract

This paper presents a fully blind phase-aware expectation-maximization (EM) algorithm for OFDM systems with the phase-shift keying (PSK) modulation. We address the well-known local maximum problem of the EM algorithm for blind channel estimation. This is primarily caused by the unknown phase ambiguity in the channel estimates, which conventional blind EM estimators cannot resolve. To overcome this limitation, we propose to exploit the extrinsic information from the decoder as model evidence metrics. A finite set of candidate models is generated based on the inherent symmetries of PSK modulation, and the decoder selects the most likely candidate model. Simulation results demonstrate that, when combined with a simple convolutional code, the phase-aware EM algorithm reliably resolves phase ambiguity during the initialization stage and reduces the local convergence rate from 80% to nearly 0% in frequency-selective channels with a constant phase ambiguity. The algorithm is invoked only once after the EM initialization stage, resulting in negligible additional complexity during subsequent turbo iterations.