Data Analytics in G3-PLC Deployments for Coverage Prediction
2021 IEEE International Symposium on Power Line Communications and its Applications, ISPLC 2021, Page: 114-119
2021
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Power Line Communications (PLC) are the communication technology at the base of Smart Grid operation. They rely on the preexisting infrastructure that enables power delivery. To evaluate performance of this technology, usually the main metric that is looked at is the SNR at the receiver in a link. In order to predict this performance, different approaches can be used: bottom-up approaches implement physical models to employ characteristics of the medium to understand its channel response, while top-down ones focus on data from measurements to identify patterns and create stochastic models.Due to a hard-to-model noise and channel, these approaches come up short. In this work, we consider measurement data from Low Voltage distribution networks, we show how the classic SNR value relates to the network topology; additionally, we discuss how coverage in terms of distance from the central element of the network can be used as a performance indicator and how it relates to a novel, easy-to-compute density factor.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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