Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications
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Abstract (arXiv)
In this paper, we highlight recent advances in the use of machine learning for implementing equalizers for optical communications. We highlight both algorithmic advances as well as implementation aspects using conventional and neuromorphic hardware.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 15 Nov 2024.
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References · 11
- 2019Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoderscited by 11
- 2022Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoderscited by 7
- 2022Spiking Neural Network Decision Feedback Equalizationcited by 3
- 2023Improving the Bootstrap of Blind Equalizers with Variational Autoencoderscited by 2
- 2023Blind Channel Equalization Using Vector-Quantized Variational Autoencoderscited by 2
- 2023Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communicationcited by 2
- 2024Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communicationscited by 2
- 2023Energy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encodingcited by 1
- 2023Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoderscited by 1
- 2024CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecturecited by 1
- 2024Achieving High Throughput with a Trainable Neural-Network-Based Equalizer for Communications on FPGAcited by 1
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