End-to-End Optical Semantic Communication over a Nonlinear WDM Fiber Link

Abstract:Emerging optical-network applications increasingly use received data for inference and control rather than exact source reproduction, creating an opportunity to trade bit-level fidelity for greater transmission reach and efficiency. We propose an end-to-end optical semantic communication system for joint image classification and reconstruction over a nonlinear wavelength-division multiplexed (WDM) fiber channel. The system maps each image directly into a fixed-length sequence of channel symbols that preserves task-relevant information, without explicit source compression or channel coding. Experiments on the MNIST dataset cover launch powers from -9 to +3 dBm, fiber lengths up to 800 km, and 16-, 64-, and 256-Quadrature Amplitude Modulation (QAM) formats. At 0 dBm, classification accuracy remains between 98.92% and 99.31% across all tested link lengths and modulation orders, while requiring fewer transmitted symbols than a Low-Density Parity-Check (LDPC)-coded JPEG baseline at every tested modulation order. These results show that semantic communication can simultaneously extend optical reach and reduce transmission resources by conveying only task-relevant information.

Signal Processing (eess.SP); Information Theory (cs.IT); Machine Learning (cs.LG)

arXiv:2609.37551 [eess.SP]

(or arXiv:2609.37551v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2609.37551 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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