Electromagnetic Compatibility

Hirsch, Holger; Koch, Michael; Weling, Nico; Zeddam, A.

doi:10.1002/9781118676684.ch3

Cited by 2 publications

(1 citation statement)

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“…II-C) are promising tools to generate PLC synthetic noise while taking into account time dependence. In addition, another area where ML can provide help is modeling electro-magnetic-interference and mode conversions occurring along the medium on the signal [118]. Radiation in PLC occurs at the network discontinuities and its radiation pattern depends on the length of each branch.…”

Section: B Statistical-synthetic Modelingmentioning

confidence: 99%

Machine Learning Tips and Tricks for Power Line Communications

et al. 2019

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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.

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Section: B Statistical-synthetic Modelingmentioning

confidence: 99%