# Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications

arXiv: https://arxiv.org/abs/2109.08711 · HTML: https://mlsanity.com/paper/2109.08711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Lower Bounds for Smooth Nonconvex Finite-Sum Optimization](https://mlsanity.com/paper/1901.11224/) (2019)
- [Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2103.08212/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction](https://mlsanity.com/paper/2306.14683/) (2023)

## Abstract (arXiv)

Addressing the neural network-based optical channel equalizers, we quantify the trade-off between their performance and complexity by carrying out the comparative analysis of several neural network architectures, presenting the results for TWC and SSMF set-ups.