Capacity Learning for Communication Systems over Power Lines

The development of power line communication (PLC) systems and algorithms is significantly challenged by the presence of unconventional noise. The analytic description of the PLC noise has always represented a formidable task and less or nothing is known about optimal channel coding/decoding schemes for systems affected by such type of noise. Recently, deep learning techniques have shown promising results and a wide range of opportunities in areas where a mathematical description of the physical phenomenon is not attainable. In this sense, the complex nature of the PLC network renders its medium characterization extremely challenging and therefore appealing for a data-driven approach. In this paper, we present a statistical learning framework to estimate the capacity of additive noise channels, for which no closed form or numerical expressions are available. In particular, we study the capacity of a PLC medium under Nakagami-m noise and determine the optimal symbol distribution that approaches it. We lastly provide insights on how to extend the framework to any real PLC system for which a noise measurement campaign has been conducted. Numerical results demonstrate the potentiality of the proposed methods.