The main subject of this paper is the sensing of network anomalies that span from harmless impedance changes at some network termination to more or less pronounced electrical faults, considering also cable degradation over time. In this paper, we present how to harvest information about such anomalies in distribution grids using high frequency signals spanning from few kHz to several MHz. Given the wide bandwidth considered, we rely on power line modems as network sensors. We firstly discuss the front-end architectures needed to perform the measurement and then introduce two algorithms to detect, classify and locate the different kinds of network anomalies listed above. Simulation results are finally presented. They validate the concept of sensing in smart grids using power line modems and show the efficiency of the proposed algorithms.

We consider a bit-interleaved coded wideband impulse-modulated system for power line communications. Impulse modulation is combined with direct-sequence code-division multiple access (DS-CDMA) to obtain a form of orthogonal modulation and to multiplex the users. We focus on the receiver signal processing algorithms and derive a maximum likelihood frequency-domain detector that takes into account the presence of impulse noise as well as the intercode interference (ICI) and the multiple-access interference (MAI) that are generated by the frequency-selective power line channel. To reduce complexity, we propose several simplified frequency-domain receiver algorithms with different complexity and performance. We address the problem of the practical estimation of the channel frequency response as well as the estimation of the correlation of the ICI-MAI-plus-noise that is needed in the detection metric. To improve the estimators performance, a simple hard feedback from the channel decoder is also used. Simulation results show that the scheme provides robust performance as a result of spreading the symbol energy both in frequency (through the wideband pulse) and in time (through the spreading code and the bit-interleaved convolutional code).

The authors consider the secure transmission of information over power line communication (PLC) networks. The focus is on the secrecy guaranteed at the physical layer, named physical layer security (PLS). Although PLS has been deeply investigated for the wireless case, it is not the same for the PLC environment. Thus, starting from the knowledge in the wireless context, the authors extend the results to typical PLC scenarios. In particular, the PLC channel statistics is evaluated and a performance comparison among PLC and wireless channels is performed, in terms of secrecy rate distribution. For the PLC case, the secrecy rate distribution, under a total power constraint, is evaluated for both optimal and uniform power distributions in broadband channels. To provide experimental evidence, the authors consider channel measures obtained in an in‐home measurement campaign. The underlying network presents a tree topology, which introduces frequency and spatial correlation among channels, and suffers from the keyhole effect, generated by branches that depart from the same node. As shown by the numerical results, these effects can reduce the secrecy rate. Finally, the authors evaluate the secrecy rate region for the multi‐user broadcast channel considering both simulated channel realisations and experimental channel measures.

The autoencoder concept has fostered the reinterpretation and the design of modern communication systems. It consists of an encoder, a channel and a decoder block that modify their internal neural structure in an end-to-end learning fashion. However, the current approach to train an autoencoder relies on the use of the cross-entropy loss function. This approach can be prone to overfitting issues and often fails to learn an optimal system and signal representation (code). In addition, less is known about the autoencoder ability to design channel capacity-approaching codes, i.e., codes that maximize the input-output mutual information under a certain power constraint. The task being even more formidable for an unknown channel for which the capacity is unknown and therefore it has to be learnt. In this paper, we address the challenge of designing capacity-approaching codes by incorporating the presence of the communication channel into a novel loss function for the autoencoder training. In particular, we exploit the mutual information between the transmitted and received signals as a regularization term in the cross-entropy loss function, with the aim of controlling the amount of information stored. By jointly maximizing the mutual information and minimizing the cross-entropy, we propose a theoretical approach that a) computes an estimate of the channel capacity and b) constructs an optimal coded signal approaching it. Theoretical considerations are made on the choice of the cost function and the ability of the proposed architecture to mitigate the overfitting problem. Simulation results offer an initial evidence of the potentiality of the proposed method.

The aim of the present work is to provide the theoretical fundamentals needed to monitor power grids using high frequency sensors. In our context, network monitoring refers to the harvesting of different kinds of information: topology of the grid, load changes, presence of faults and cable degradation. We rely on transmission line theory to carry out a thorough analysis of how high frequency signals, such those produced by power line communication modems, propagate through multi-conductor power networks. We also consider the presence of electrical anomalies on the network and analyze how they affect the signal propagation. In this context, we propose two models that rely on reflectometric and end-to-end measurements to extrapolate information about possible anomalies. An in-depth discussion is carried out to explain the properties of each model and measurement method, in order to enable the development of appropriate anomaly detection and location algorithms.