Optical Communications with Relative Intensity Noise: Channel Modeling and Information Rates

2026-03-09

AI Summary

This study addresses the signal-dependent noise and channel memory induced by laser relative intensity noise (RIN) in intensity-modulated direct-detection optical communication systems. Starting from a continuous-time waveform, the authors develop a discrete-time channel model incorporating RIN effects and analyze its achievable information rates using a mismatched decoding framework. The analysis reveals that RIN causes the conditional noise variance to depend polynomially on the transmitted symbol, thereby violating the conventional constant-variance assumption. Furthermore, neglecting channel memory leads to saturation of the generalized mutual information (GMI) with increasing constellation order, elucidating the performance degradation observed in high-order modulation formats. Numerical results confirm that this phenomenon stems from the asymmetric and non-vanishing contributions of individual symbols to the GMI.

Technology Category

• Machine Learning: Information Theory
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Security and Privacy: Large-scale security measurements
• 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

Abstract

We consider optical communications with intensity modulation and direct detection affected by laser relative intensity noise (RIN). Starting from a continuous-time waveform model, we derive an equivalent discrete-time channel model. As a result of RIN, the resulting channel model exhibits signal-dependent noise with memory. Unlike the commonly-assumed model in the literature, the conditional variance of this noise term has a polynomial dependence on the symbol of interest. Finally, we study achievable information rates for this channel under practically-relevant system parameters. We take a mismatched decoding approach and compute the generalized mutual information (GMI) using a memoryless decoding metric. Our numerical results show that when the memory in the channel is ignored by the receiver, GMI saturates as the constellation size increases, and thus, dense constellations do not offer gains. We also show that this saturation results from nonsymmetric nonvanishing contributions of the symbols to the GMI.

Problem

• Optical Communications
• Relative Intensity Noise
• Signal-Dependent Noise
• Channel Modeling
• Information Rates

Innovation

• Relative Intensity Noise (RIN)
• Signal-dependent noise with memory
• Generalized Mutual Information (GMI)
• Mismatched decoding
• Discrete-time channel modeling