Artificial intelligence based routing in PLC networks

Marcuzzi, Francesco; Tonello, Andrea M.

doi:10.1109/isplc.2018.8360241

Cited by 4 publications

(2 citation statements)

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“…In more detail, the envisioned application aims at using the power line distribution network as a fronthaul infrastructure for a small cell radio network. In [134] the small cell radio network and the underlying power line infrastructure are brought together in a joint paradigm and the capacity of the PLC fronthaul is analyzed through a bottom-up emulation tool [114], [135]. A regression approach is developed to tackle the problem of determining the capacity of an end-to-end power line link based solely on geometrical/topological properties of the network, for instance the density of nodes (radio cells) in the overall service area, and the distance between the communicating nodes.…”

Section: ML For Mac and Network Layer Plcmentioning

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: ML For Mac and Network Layer Plcmentioning

confidence: 99%

Machine Learning Tips and Tricks for Power Line Communications

et al. 2019

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“…The random network generator in itself is also a tool to evaluate characteristics of real networks. PLC are fundamental for SG, and there have been already considerations regarding the dependency between topology and network performance [8]- [14], which we started from to develop this work. Network performance is very strongly related to the channel quality.…”

Section: Introductionmentioning

confidence: 99%

Topology-Based Machine Learning: Predicting Power Line Communication Quality in Smart Grids

Marcuzzi

Tonello

2023

IEEE Access

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Smart Grids (SG) envision the exchange of both power and data, enabling system and customers to generate and transfer energy in a more efficient and balanced way. Among the relevant communication technologies, we find Power-Line Communications (PLC), which allow for data transmission on the electrical cables used for power delivery. Despite the hostile medium, PLC offer reliability and data rates to support exchange of control traffic, smart metering and polling applications. The distribution portion of the power delivery network, which we focus on in this work, is the most topologically complex, which makes channel prediction complicated. We show how random realistic topologies can be generated and then used to train a Machine Learning (ML) algorithm to infer PLC link quality (based on channel response) based solely on topology descriptors. We eventually show how precisely the communication quality can be inferred from the SG topology through ML. In doing so, we also discuss how the ML approach offers the common ground between top-down and bottom-up approaches for network characterization and how it enables smart decision making in the SG.

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Artificial-Intelligence-Based Performance Enhancement of the G3-PLC LOADng Routing Protocol for Sensor Networks

Marcuzzi

Tonello

2019

2019 IEEE International Symposium on Power Line Communications and Its Applications (ISPLC)

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Artificial intelligence based routing in PLC networks

Cited by 4 publications

References 12 publications

Machine Learning Tips and Tricks for Power Line Communications

Machine Learning Tips and Tricks for Power Line Communications

Topology-Based Machine Learning: Predicting Power Line Communication Quality in Smart Grids

Artificial-Intelligence-Based Performance Enhancement of the G3-PLC LOADng Routing Protocol for Sensor Networks

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