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

2025-11-26

AI Summary

In PSK-modulated OFDM systems, blind channel estimation via the EM algorithm suffers from phase ambiguity, leading to high susceptibility to local optima. Method: This paper proposes a phase-aware aided-code EM algorithm that leverages extrinsic information from the decoder as model evidence and exploits the rotational symmetry of PSK constellations to construct and prune a candidate channel model set during initialization—thereby precisely resolving phase ambiguity. The algorithm is embedded within a convolutional-coded Turbo iterative framework to jointly optimize channel estimation and decoding. Results: Experiments under frequency-selective fading channels demonstrate that the proposed method reduces the local convergence rate from 80% to nearly 0%, with only a single additional phase decision and negligible increase in computational complexity. This significantly enhances the reliability and robustness of blind channel estimation.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Local Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Resolving phase ambiguity in blind channel estimation for PSK-OFDM systems
• Overcoming local maximum convergence issues in expectation-maximization algorithms
• Selecting optimal channel models using decoder extrinsic information metrics

Innovation

• Phase-aware EM algorithm resolves phase ambiguity
• Uses decoder extrinsic information as model evidence
• Generates candidate models from PSK symmetries