Power distribution grids provide a physical medium not only for power delivery but also for data transmission using Power Line Communication (PLC) systems. However, since such a medium has not been designed specifically for this purpose, several challenges need to be overcome to realize reliable communication schemes. The channel response is affected by multipath propagation, cyclic time variations and several forms of noise and interference are present. “The understanding of the medium characteristics is highly relevant for the scientific community. This is why we have carried out a number of experimental measurement campaigns in several different scenarios: outdoor power line networks, indoor home and industrial networks and even vehicular networks which include cars and ships,” Tonello says.
Tonello’s group obtained new findings about the statistics of the PLC channel, as for instance in the domain of in-home PLC networks exploiting frequencies up to 300 MHz, in the domain of medium voltage power grids, and of in-car networks.
The analysis of the channel characteristics led to novel modeling approaches based on a statistical representation of the channel and are considered state-of-the-art by the research community. In detail, two approaches were followed: a bottom-up, a top-down , and more recently a third new approach called synthetic.
Bottom-up channel modeling refers to an approach where the channel impulse/frequency response is obtained via the application of transmission line theory to a specified network topology, cables and loads characteristics. Conventionally, this approach is applied to obtain a specific response and it is also referred to as deterministic model. “To overcome this limitation we have proposed to use a statistical description of the network topology. In addition, to limit the computational effort, we have devised an efficient method for the computation of the channel transfer function for both SISO and MIMO channels,” Tonello comments.
The top-down method is a phenomenological approach that exploits a parametric model for the channel response. The statistics of the parameters are obtained by fitting data from measurements. “This approach has been successfully applied to model in-home SISO and MIMO channels making also use of the data obtained in collaboration with the ETSI Specialist Task Force STF-410,” Tonello explains.
The top-down method is phenomenological but it still tries to model the channel with a fitting function that has some connection with physical phenomena. “A purely phenomenological approach was not considered yet”, prof. Tonello says. “We came up with the idea of a fully phenomenological model obtained from the observation of collected data from measurements. We named the model synthetic since this terminology encompasses two meanings, namely artificial, i.e. obtained from synthesis without physical assumptions, and compact, since the model adopts a small number of parameters.” The proposed novel model adopts a completely new strategy that takes into account both the frequency and the MIMO ports (or modes) correlation, represented by the MIMO statistical covariance matrix. In particular, the proposed synthetic model implementation strategy is able to reconstruct the overall MIMO matrix by using a significantly reduced number of parameters. This implies, that the full correlation matrix that has million of coefficients obtained by measurements is not needed. “I believe that this new approach will stimulate new research endeavors in the field of PLC statistical channel modeling. In turn this will enable the development of fast simulation tools”, prof. Tonello says.
A. Pittolo and A. M. Tonello. A Synthetic Statistical MIMO PLC Channel Model Applied to an In-Home Scenario. IEEE Transactions on Communications, 2017.
A. Pittolo, M. De Piante, F. Versolatto, and A. M. Tonello. In-Vehicle Power Line Communication: Differences and Similarities Among the In-Car and the In-Ship Scenarios. IEEE Vehicular Technology Magazine, 2016.
M. Antoniali, F. Versolato, and A. M. Tonello. An Experimental Characterization of the PLC Noise at the Source. IEEE Transactions on Power Delivery, 2016.
A. M. Tonello, A. Pittolo, and M. Girotto. “Power Line Communications: Understanding the Channel for Physical Layer Evolution Based on Filter Bank Modulation. IEICE Transactions on Communications, 2014 (invited).
A. M. Tonello, F. Versolatto, and A. Pittolo. In-Home Power Line Communication Channel: Statistical Characterization. IEEE Transactions on Communications, 2014.
A. M. Tonello, F. Versolato, B. Bejar, and S. Zazo. A Fitting Algorithm for Random Modeling the PLC Channel. IEEE Transactions on Power Delivery, 2012.
F. Versolatto and A. M. Tonello. An MTL Theory Approach for the Simulation of MIMO Power Line Communication Channels. IEEE Transactions on Power Delivery, 2011.
A. M. Tonello and F. Versolatto. Bottom-Up Statistical PLC Channel Modeling – Part I: Random Topology Model and Efficient Transfer Function Computation. IEEE Transactions on Power Delivery, 2011.
A. M. Tonello and F. Versolatto. Bottom-Up Statistical PLC Channel Modeling – Part II: Inferring the Statistics. IEEE Transactions on Power Delivery, 2010.