# A Survey of Applied Machine Learning Techniques for Optical OFDM based Networks

arXiv: https://arxiv.org/abs/2105.03289 · HTML: https://mlsanity.com/paper/2105.03289/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 107 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 9 of 9)

- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [An Overview on Application of Machine Learning Techniques in Optical Networks](https://mlsanity.com/paper/1803.07976/) (2018)
- [Deep learning the high variability and randomness inside multimode fibres](https://mlsanity.com/paper/1807.09351/) (2018)
- [A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm](https://mlsanity.com/paper/1404.6059/) (2014)
- [Robust and sparse k-means clustering for high-dimensional data](https://mlsanity.com/paper/1709.10012/) (2017)
- [Photonic machine learning implementation for signal recovery in optical communications](https://mlsanity.com/paper/1710.01107/) (2017)
- [Machine Learning for Intelligent Optical Networks: A Comprehensive Survey](https://mlsanity.com/paper/2003.05290/) (2020)
- [Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems](https://mlsanity.com/paper/2003.08980/) (2020)
- [A Machine Learning-Based Detection Technique for Optical Fiber Nonlinearity Mitigation](https://mlsanity.com/paper/1903.01549/) (2019)

## Abstract (arXiv)

In this survey, we analyze the newest machine learning (ML) techniques for optical orthogonal frequency division multiplexing (O-OFDM)-based optical communications. ML has been proposed to mitigate channel and transceiver imperfections. For instance, ML can improve the signal quality under low modulation extinction ratio or can tackle both determinist and stochastic-induced nonlinearities such as parametric noise amplification in long-haul transmission. The proposed ML algorithms for O-OFDM can in particularly tackle inter-subcarrier nonlinear effects such as four-wave mixing and cross-phase modulation. In essence, these ML techniques could be beneficial for any multi-carrier approach (e.g. filter bank modulation). Supervised and unsupervised ML techniques are analyzed in terms of both O-OFDM transmission performance and computational complexity for potential real-time implementation. We indicate the strict conditions under which a ML algorithm should perform classification, regression or clustering. The survey also discusses open research issues and future directions towards the ML implementation.