# Improving the Bootstrap of Blind Equalizers with Variational Autoencoders

arXiv: https://arxiv.org/abs/2301.06576 · HTML: https://mlsanity.com/paper/2301.06576/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders](https://mlsanity.com/paper/2204.11776/) (2022)

## Built on by (most cited first, 2 of 2)

- [Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications](https://mlsanity.com/paper/2411.10101/) (2024)
- [Novel Phase-Noise-Tolerant Variational-Autoencoder-Based Equalization Suitable for Space-Division-Multiplexed Transmission](https://mlsanity.com/paper/2509.14072/) (2025)

## Abstract (arXiv)

We evaluate the start-up of blind equalizers at critical working points, analyze the advantages and obstacles of commonly-used algorithms, and demonstrate how the recently-proposed variational autoencoder (VAE) based equalizers can improve bootstrapping.