Power Line Communications: channel Characterization and Modeling

Power line communications stands for the communication technologies that aims to exploit the power delivery network for data transmission. The power delivery network was not designed for communications at high frequencies, therefore power line communication (PLC) experiences high attenuation, deep fading effects and large noise impairments. For the design of the next generation PLC algorithms and devices, the perfect knowledge of the PLC channel is fundamental. In this work we present a comprehensive overview of the characterization and modeling of the PLC channel. We mainly focus on the broadband in-home scenario, and we extend the analysis to the medium voltage (MV) lines. The latter scenario is of particular interest for smart grid applications that require robust low data rate communications. In the first part of this work, we focus on the channel characterization. We present the statistical characterization of a database of PLC channels that we collected during an experimental measurement campaign in Italy. We study the normality of the channel frequency response, the distribution of the phase and the statistics of the average channel gain (ACG)and the root-mean-square delay spread (RMS-DS). Furthermore, we show the relation between the ACG and the RMS-DS, and between the coherence bandwidth and the RMS-DS. We infer the relation between the channel statistics and the geometrical distance between the transmitter and the receiver outlet. We aim to define the PLC coverage similarly to the wireless context. In this respect, we study the maximum achievable rate of the measured channels. We also study the gain in terms of achievable rate provided by the extension of the signalling band from 2-100 MHz to 2-300 MHz. Then, we focus on the medium voltage lines. We consider a MV network where a channel measurement campaign has been carried out. We firstly describe the MV test network. We study the statistics of the MV channels in terms of both RMS-DS and ACG and we identify three channels that are representative of the worst average and best case in terms of maximum achievable rate. Finally, we exploit the results of the statistical analysis on RMS-DS lines to design a impulsive-ultra wideband (I-UWB) system for low data-rate command and control applications. We describe the I-UWB system model, and we infer the performance assuming perfect knowledge of the channel response and the synchronization instant at the receiver side. Finally, we introduce the non-idealities and we compare practical receiver algorithms. In the second part of this work, we address channel modeling from both the bottom-up and the top-down perspective. Firstly, we present a novel bottom-up random channel generator that is based on a random topology generation algorithm and an efficient method to compute the channel transfer function in complex networks. The bottom-up approach is fundamental when the topological information is required, as, for instance, in the study of relaying. Then, we propose a multiconductor extension of the model. We validate the method comparing the simulation to the measures, and we exploit the tool to infer the performance improvement provided by the use of multiple output transmission schemes in PLC. Finally, we present a novel fitting procedure to initialize a random top-down channel generator in order to reproduce the statistics of a set of measured channels. 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 channel transfer function and the statistical correlation function. 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 is 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 whole 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 is also studied, and it is compared to the results of experimental measurement campaigns in the literature.