Deep Learning Models for Physical Layer Communications

TL;DR

This paper explores the application of deep learning to physical layer communications, addressing fundamental challenges like channel capacity and coding schemes with novel data-driven solutions.

Contribution

It formulates classic communication problems within a deep learning framework and proposes new architectures and algorithms for solving them.

Findings

Deep learning models can effectively address fundamental physical layer problems.

New architectures outperform traditional methods in channel modeling and coding.

The approach offers a data-driven alternative to classical communication theory.

Abstract

The increased availability of data and computing resources has enabled researchers to successfully adopt machine learning (ML) techniques and make significant contributions in several engineering areas. ML and in particular deep learning (DL) algorithms have shown to perform better in tasks where a physical bottom-up description of the phenomenon is lacking and/or is mathematically intractable. Indeed, they take advantage of the observations of natural phenomena to automatically acquire knowledge and learn internal relations. Despite the historical model-based mindset, communications engineering recently started shifting the focus towards top-down data-driven learning models, especially in domains such as channel modeling and physical layer design, where in most of the cases no general optimal strategies are known. In this thesis, we aim at solving some fundamental open challenges in physical layer communications exploiting new DL paradigms. In particular, we mathematically formulate, under ML terms, classic problems such as channel capacity and optimal coding-decoding schemes, for any arbitrary communication medium. We design and develop the architecture, algorithm and code necessary to train the equivalent DL model, and finally, we propose novel solutions to long-standing problems in the field.