Symbol Detection in Inter-Symbol Interference Channels using Expectation Propagation with Channel Shortening

2025-09-22

AI Summary

Conventional expectation propagation (EP) detection suffers severe performance degradation in strongly intersymbol-interference (ISI) channels due to inaccurate initial linear minimum mean-square-error (LMMSE) estimates. Method: This paper proposes a transform-domain iterative message-passing detection framework. It incorporates channel-shortening filtering to compress the time-domain impulse response and jointly optimizes linear EP and low-complexity BCJR detection in the transform domain. A deliberate initialization mismatch strategy is introduced to accelerate convergence, and symbol-level nonlinear mapping is replaced by a BCJR detector with controllable state complexity to enhance robustness. Contribution/Results: The method significantly improves message covariance estimation and prior matching. Experiments on Proakis-C and measured wireless channels demonstrate up to 6 dB bit-error-rate gain over baseline schemes, support 2 bits per channel use, and achieve superior performance–complexity trade-offs.

Technology Category

• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Iterative message passing detection based on expectation propagation(EP) has demonstrated near-optimum performance in many signal processing and communication scenarios. The method remains feasible even for channel impulse responses (CIRs), where the optimal Bahl-Cocke-Jelinek-Raviv (BCJR) detector is infeasible. However, significant performance degradation occurs for channels with strong inter-symbol interference (ISI), where the initial linear minimum mean square error (LMMSE) estimate is inaccurate. We propose an EP-based detector that operates in a transformed signal space obtained by channel shortening. Specifically, instead of the conventional approach that iterates between an LMMSE estimator and a non-linear symbol-wise demapper, the proposed method iterates between a linear channel shortening filter-based estimator and a nonlinear BCJR detector with reduced memory compared to the actual channel. Additionally, we propose a deliberate mismatch between the initialized messages and the initialized covariance used in the linear estimator in the first iteration for faster convergence. The proposed approach is evaluated for the well-known Proakis-C ISI channel and for CIRs from a wireless measurement campaign. We demonstrate improvements of up to 6dB at 2 bits per channel use and an improved performance-complexity trade-off over conventional EP-based detection.

Problem

• Improving symbol detection in strong ISI channels
• Reducing complexity of optimal BCJR detector implementation
• Enhancing convergence speed and performance of EP algorithms

Innovation

• Channel shortening filter replaces LMMSE estimator
• Iterates between linear filter and reduced-memory BCJR
• Deliberate message-covariance mismatch for faster convergence