Feedforward Phase Noise Compensation for Intersymbol Interference Channels

2026-04-20

AI Summary

This work addresses the performance degradation caused by phase noise in intersymbol interference (ISI) channels and proposes a non-iterative feedforward phase noise compensation method based on the sum-product algorithm (SPA). The approach models the received signal as independent Gaussian random variables and incorporates the von Mises distribution to characterize phase mismatch. To the best of our knowledge, this is the first application of non-iterative SPA to phase noise compensation in ISI channels. Under comparable computational complexity, the proposed scheme significantly outperforms conventional linear filtering techniques across various channel conditions—including ISI-free, standard single-mode fiber, and OFDM multipath channels—achieving higher information transmission rates.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

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

Abstract

A non-iterative phase noise compensation method based on the sum-product algorithm (SPA) is applied to the outputs of intersymbol interference (ISI) channels. The outputs are modeled as independent Gaussian random variables, and the receiver applies mismatched processing with von Mises statistics. The performance is compared with that of linear minimum-mean-square-error filtering. The SPA achieves higher information rates at similar complexity for three channel types: ISI-free, standard single-mode fiber, and multipath channels with orthogonal frequency-division multiplexing.

Problem

• phase noise compensation
• intersymbol interference
• information rates
• Gaussian random variables
• mismatched processing

Innovation

• phase noise compensation
• sum-product algorithm
• intersymbol interference
• von Mises distribution
• non-iterative