Communication Dans Un Congrès Année : 2025

Joint Transmitter-Receiver Optimization for Optical Communication over Nonlinear Channels

Résumé

In linear additive white Gaussian noise (AWGN) channels, optimal signaling schemes can be derived directly from established theoretical models. However, fiber-optic channels are nonlinear, making it challenging to derive optimal signaling schemes analytically. Additionally, the nonlinear behavior of electro-optic modulators and lasers under direct modulation further complicate signal optimization in fiber-optic communication. This paper demonstrates how machine learning techniques can be leveraged to jointly optimize constellations, pulse shaping, and receiver filters for fiber-optic channels. By learning optimal signal strategies tailored to specific channel characteristics, significant performance improvements are achievable.

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Dates et versions

hal-05249054 , version 1 (10-09-2025)

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Citer

Sergio Hernandez, Svitlana Matsenko, Søren Nielsen, Mikkel Schmidt, Christophe Peucheret, et al.. Joint Transmitter-Receiver Optimization for Optical Communication over Nonlinear Channels. 2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), IEEE, May 2025, Barcelona, Spain. pp.1-5, ⟨10.1109/ICMLCN64995.2025.11140405⟩. ⟨hal-05249054⟩
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