We propose an efficient bottom-up power-line communication (PLC) channel simulator that exploits transmission-line theory concepts and that is able to generate statistically representative in-home channels. We first derive from norms and practices a statistical model of European in-home topologies. The model describes how outlets are arranged in a topology and are interconnected via intermediate nodes referred to as derivation boxes. Then, we present an efficient method to compute the channel transfer function between any pair of outlets belonging to a topology realization. The method is based on a systematic remapping technique that leads to the subdivision of the network in elementary units, and on an efficient way to compute the unit transfer function referred to as the voltage ratio approach. The difference from the more conventional and complex ABCD matrix approach is also discussed. We finally show that the simulator can be configured with a small set of parameters and that it offers a theoretical framework to study the statistical PLC channel properties as a function of the topology characteristics, which is discussed in Part II of this work.

Bottom-Up Statistical PLC Channel Modeling—Part I: Random Topology Model and Efficient Transfer Function Computation

We propose a bottom-up power-line communication (PLC) channel simulator for networks that deploy multiconductor cables and enable establishing a multiple-input multiple-output (MIMO) communication link between two nodes. We show that the fundamental multiconductor transmission-line (MTL) relations are a matrix form extension of the two-conductor transmission-line equations, and that they allow the application of the voltage ratio approach for the computation of the channel transfer function (CTF). Thus, any complex network can be remapped to obtain a simple representation in terms of MTL elementary units. Then, the MIMO CTF is computed as the product of the insertion loss of the units. We discuss the analytical computation of the per-unit-length (p.u.l.) parameters for two electrical cables, that we refer to as symmetric and ribbon. Further, we propose using an improved cable model for ribbon cables that accounts for the dielectric nonuniformity. We report the comparison between simulation and experimental measures for two test networks. The results are in good agreement. This validates the proposed MIMO PLC channel simulation approach.

An MTL Theory Approach for the Simulation of MIMO Power-Line Communication Channels

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.

http://oa.upm.es/16777/1/INVE_MEM_2012_137073.pdf

A Fitting Algorithm for Random Modeling the PLC Channel

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 get 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 correla tion 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 is available in the literature The measured cha nnels 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 hetero- geneity 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

A Fitting Algorithm for Random Modeling the PLC

We consider the use of a relay to provide capacity improvements and range extension for in-home power line communication networks. In particular, we focus on opportunistic relaying where the relay is exploited only if it provides improved capacity w.r.t. the use of direct transmission between the source and the destination. The relay applies a decode and forward scheme and the channel is shared in a time division multiple access mode. The performance is studied in statistically representative in-home power line communication (PLC) networks via the use of a statistical topology model together with the application of transmission line theory for the computation of the channel transfer function among network nodes. The statistical topology model allows determining the capacity improvements as a function of the relay position. Furthermore, we determine the optimal time slot duration for each considered relay configuration, as well as we propose the use of a globally optimal time slot duration that maximizes the average network capacity. The numerical results show that significant capacity improvement can be obtained via opportunistic relaying in in-home PLC networks. The gains are more significant for low SNR scenarios and for networks composed by sub-networks each connected to the main panel via a circuit breaker that introduces signal attenuation.

http://web.diegm.uniud.it/tonello/PAPERS/CONFERENCES/GLOBECOM2010.pdf

Opportunistic Relaying in In-Home PLC Networks

We follow a bottom-up approach for modelling the channel transfer function aiming at realizing a fast statistical channel simulator. The simulator is derived from transmission line theory. It computes the transfer function via a fast procedure that calculates the voltage ratio between the receiver port and the transmitter port given a real or randomly generated network topology. The approach allows not only taking into account the cable characteristics, the number and length of branches, but also the effect of loads. In particular the effect of time-variant loads that cause the channel transfer function to be time-variant.

http://www.diegm.uniud.it/tonello/PAPERS/CONFERENCES/ISPLC2009_4.pdf

Tao Zheng

Papers

Bottom-up transfer function generator for broadband PLC statistical channel modeling