Machine Learning Applied to Optical Communication Systems

doi:10.3390/books978-3-7258-4095-3

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Keywords

optical fiber communication; self-coherent detection; machine learning; short-reach transmission; AMCC; WDM-PON; GMM; interference model; probabilistic shaping; quantum-dot (QD) spin-vertical-cavity surface-emitting laser; photonic reservoir computing; chaotic synchronization; coherent optical chaos secure communication; silicon photonics (SiPh); silicon-on-insulator (SOI); pulse amplitude modulation (PAM); silicon microring modulator (SiMRM); optical neural network (ONN); machine learning; optical communications; passive optical network; equalization; optical performance monitoring; modulation format identification; bit-error ratio; optical signal-to-noise ratio; nonlinearities; visible light communication (VLC); autoencoder (AE); photodetector (PD); color-shift keying (CSK); optical wireless communication (OWC); intensity-modulated direct-detection; neural network; training; equalization; computational complexity; optical wireless communications; vertical-cavity surface-emitting lasers; multilevel pulse amplitude modulation; digital equalisation; neural network; multilayer perceptron; CNN; VLC; VLP; OCC; indoor positioning; attention mechanism; TI; smart resilience; NG-EPON; AI; SDN; SEBA; VOLTHA; RFoG; system performance; machine learning; optical communications; VCSEL-based optical interconnects; end-to-end learning; wavelength division multiplexing; optical fiber communication; machine learning; variational auto encoder; knowledge distillation; multi-fiber optical networks; artificial neural networks; machine learning; network capacity and cost; fiber assignment; nonlinear Schrödinger equation; regular perturbation model; fiber nonlinearity; learnable parameters; single-span dual-polarization systems; n/a

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Machine Learning Applied to Optical Communication Systems

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June 2025

244 pages

ISBN 978-3-7258-4096-0 (Hardback)
ISBN 978-3-7258-4095-3 (PDF)

https://doi.org/10.3390/books978-3-7258-4095-3 (registering)

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This is a Reprint of the Special Issue Machine Learning Applied to Optical Communication Systems that was published in

Engineering

Summary

This reprint explores how machine learning is revolutionizing optical communication systems, featuring topics ranging from long-haul coherent links and short-reach IM/DD systems to PONs, VLC, and chaos-based secure communication. Covering neural network equalization, optical signal processing, and system-level intelligence, this collection highlights cutting-edge research and future directions at the intersection of AI and photonics.