Machine Learning Tips and Tricks for Power Line Communications
It addresses the integration of ML into PLC for improved communication and grid diagnostics, but is incremental as it reviews and motivates rather than introduces new methods.
This paper explores the application of machine learning techniques to Power Line Communications, discussing potential uses across various layers such as characterization, physical layer algorithms, and networking, with illustrative numerical examples provided for validation.
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.