# Machine Learning Tips and Tricks for Power Line Communications

## TL;DR

This paper explores how machine learning techniques can enhance power line communication systems across various layers, providing insights, applications, and numerical validations to guide future research.

## Contribution

It offers a comprehensive overview of ML applications in PLC, including modeling, algorithms, and diagnostics, highlighting new integration opportunities.

## Key findings

- ML improves PLC modeling accuracy
- ML-based algorithms enhance physical layer performance
- Numerical examples validate ML benefits in PLC

## Abstract

A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application fields. This paper provides a vision of what ML can do in Power Line Communications (PLC). We firstly and briefly describe classical formulations of ML, and distinguish deterministic from statistical learning models with relevance to communications. We then discuss ML applications in PLC for each layer, namely, for characterization and modeling, for the development of physical layer algorithms, for media access control and networking. Finally, other applications of PLC that can benefit from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical examples are reported to serve the purpose of validating the ideas and motivate future research endeavors in this stimulating signal/data processing field.