Search Results for author:
Found 11 papers, 4 papers with code
On the Statistical Analysis of the Multipath Propagation Model Parameters for Power Line Communications
This paper proposes a fitting procedure that aims to identify the statistical properties of the parameters that describe the most widely known multipath propagation model (MPM) used in power line communication (PLC).
$f$-Divergence Based Classification: Beyond the Use of Cross-Entropy
In addition, driven by the challenge of improving the state-of-the-art approach, we propose a bottom-up method that leads us to the formulation of a new objective function (and posterior probability estimator) corresponding to a novel $f$-divergence referred to as shifted log (SL).
Variational $f$-Divergence and Derangements for Discriminative Mutual Information Estimation
We propose a novel class of discriminative mutual information estimators based on the variational representation of the $f$-divergence.
An Asymptotically Equivalent GLRT Test for Distributed Detection in Wireless Sensor Networks
Nevertheless, its asymptotic performance is proved to be identical to the original GLRT, showing that the statistically dependence of the measurements has no impact on the detection performance in the asymptotic scenario.
Copula Density Neural Estimation
Probability density estimation from observed data constitutes a central task in statistics.
An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem.
MIND: Maximum Mutual Information Based Neural Decoder
The computation of the a-posteriori information is a formidable task, and for the majority of channels it is unknown.
Discriminative Mutual Information Estimators for Channel Capacity Learning
This is because it requires to carry out two formidable tasks a) the computation of the mutual information between the channel input and output, and b) its maximization with respect to the signal distribution at the channel input.
Capacity-Approaching Autoencoders for Communications
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.
Machine Learning Tips and Tricks for Power Line Communications
A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application fields.
State-of-the-art in Power Line Communications: from the Applications to the Medium
In this article we provide an overview of both narrowband and broadband systems, covering potential applications, regulatory and standardization efforts and recent research advancements in channel characterization, physical layer performance, medium access and higher layer specifications and evaluations.
Networking and Internet Architecture
