A compilation of deep learning methods for channel modeling, neural decoding, mutual information estimation, and capacity learning, applied to power line communications.
Copula Density Neural Estimation
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abstract
Probability density estimation from observed data constitutes a central task in statistics. In this brief, we focus on the problem of estimating the copula density associated to any observed data, as it fully describes the dependence between random variables. We separate univariate marginal distributions from the joint dependence structure in the data, the copula itself, and we model the latter with a neural network-based method referred to as copula density neural estimation (CODINE). Results show that the novel learning approach is capable of modeling complex distributions and can be applied for mutual information estimation and data generation.
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Deep Learning Models for Physical Layer Communications
A compilation of deep learning methods for channel modeling, neural decoding, mutual information estimation, and capacity learning, applied to power line communications.