Machine Learning Tips and Tricks for Power Line Communications

Abstract

A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application elds. This paper provides a vision of what ML can do in Power Line Communications (PLC). We rst and brie y describe classical formulations of the 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 the PLC that can bene t 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 eld.