A fitting algorithm for random modeling the PLC channel

Tonello, Andrea and Versolatto, Fabio and Béjar Haro, Benjamín and Zazo Bello, Santiago (2012). A fitting algorithm for random modeling the PLC channel. "IEEE Transactions on Power Delivery", v. 27 (n. 3); pp. 1477-1484. ISSN 0885-8977.


Title: A fitting algorithm for random modeling the PLC channel
  • Tonello, Andrea
  • Versolatto, Fabio
  • Béjar Haro, Benjamín
  • Zazo Bello, Santiago
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Power Delivery
Date: July 2012
Volume: 27
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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The characteristics of the power-line communication (PLC) channel are difficult to model due to the heterogeneity of the networks and the lack of common wiring practices. To obtain the full variability of the PLC channel, random channel generators are of great importance for the design and testing of communication algorithms. In this respect, we propose a random channel generator that is based on the top-down approach. Basically, we describe the multipath propagation and the coupling effects with an analytical model. We introduce the variability into a restricted set of parameters and, finally, we fit the model to a set of measured channels. The proposed model enables a closed-form description of both the mean path-loss profile and the statistical correlation function of the channel frequency response. As an example of application, we apply the procedure to a set of in-home measured channels in the band 2-100 MHz whose statistics are available in the literature. The measured channels are divided into nine classes according to their channel capacity. We provide the parameters for the random generation of channels for all nine classes, and we show that the results are consistent with the experimental ones. Finally, we merge the classes to capture the entire heterogeneity of in-home PLC channels. In detail, we introduce the class occurrence probability, and we present a random channel generator that targets the ensemble of all nine classes. The statistics of the composite set of channels are also studied, and they are compared to the results of experimental measurement campaigns in the literature.

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Item ID: 16777
DC Identifier: http://oa.upm.es/16777/
OAI Identifier: oai:oa.upm.es:16777
Deposited by: Memoria Investigacion
Deposited on: 10 Aug 2013 09:41
Last Modified: 21 Apr 2016 17:07
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