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Stochastic modeling of signal propagation in power-line communication networks

SUMMARY Signal propagation through power‐line networks has been studied by a number of researchers. Among a number of propagation models described in literature, deterministic models based on actual physical description of the network can be constructed as both very accurate and very efficient in co...

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Bibliographic Details
Published in:International journal of communication systems 2014-11, Vol.27 (11), p.3128-3146
Main Authors: Sabolic, Dubravko, Car, eljka
Format: Article
Language:English
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Summary:SUMMARY Signal propagation through power‐line networks has been studied by a number of researchers. Among a number of propagation models described in literature, deterministic models based on actual physical description of the network can be constructed as both very accurate and very efficient in computational terms. Yet they have an inherent drawback of being suitable for propagation analyses in static conditions and steady state only. Thus, our main research problem was how to extend a deterministic frequency‐domain‐based propagation model for a more practically useful modeling of channels of multi‐port power‐line communication networks. We have concentrated on a particular model that we presented in an earlier literature. Our main findings are as follows: Computationally efficient deterministic models can be utilized for stochastic simulations in multi‐port power‐line network environments by repeating the propagation simulation routine virtually as many times as needed, to model the network parameter variability by appropriate stochastic modeling of termination impedances connected to each of the multiple network ports. In this way, an extended set of physical properties of the channel can be simulated and statistically analyzed, such as the complex transfer function, impulse response, delay spread, and group delay. Copyright © 2013 John Wiley & Sons, Ltd. A deterministic frequency‐domain‐based propagation model for power‐line communication (PLC) networks was extended to serve as an efficient stochastic modeling tool for simulation of signal propagation in complex multi‐port PLC networks.
ISSN:1074-5351
1099-1131
DOI:10.1002/dac.2530