# Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications

arXiv: https://arxiv.org/abs/2411.10101 · HTML: https://mlsanity.com/paper/2411.10101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders](https://mlsanity.com/paper/1905.08795/) (2019)
- [Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders](https://mlsanity.com/paper/2204.11776/) (2022)
- [Spiking Neural Network Decision Feedback Equalization](https://mlsanity.com/paper/2211.04756/) (2022)
- [Improving the Bootstrap of Blind Equalizers with Variational Autoencoders](https://mlsanity.com/paper/2301.06576/) (2023)
- [Blind Channel Equalization Using Vector-Quantized Variational Autoencoders](https://mlsanity.com/paper/2302.11687/) (2023)
- [Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication](https://mlsanity.com/paper/2302.14726/) (2023)
- [Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications](https://mlsanity.com/paper/2409.08698/) (2024)
- [Energy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encoding](https://mlsanity.com/paper/2312.12909/) (2023)
- [Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders](https://mlsanity.com/paper/2312.16003/) (2023)
- [CNN-Based Equalization for Communications: Achieving Gigabit Throughput with a Flexible FPGA Hardware Architecture](https://mlsanity.com/paper/2405.02323/) (2024)
- [Achieving High Throughput with a Trainable Neural-Network-Based Equalizer for Communications on FPGA](https://mlsanity.com/paper/2407.02967/) (2024)

## 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.