Physical Layer Communications System Design Over-the-Air Using Adversarial Networks

TL;DR

This paper introduces an adversarial learning-based method for designing physical layer communication systems that can adapt to unknown or complex channel impairments without relying on explicit channel models.

Contribution

It extends channel autoencoder techniques by incorporating adversarial networks to jointly learn modulation, coding, and channel response approximation in unknown environments.

Findings

Successful over-the-air training and validation

Effective adaptation to diverse channel conditions

Potential for improved communication robustness

Abstract

This paper presents a novel method for synthesizing new physical layer modulation and coding schemes for communications systems using a learning-based approach which does not require an analytic model of the impairments in the channel. It extends prior work published on the channel autoencoder to consider the case where the channel response is not known or can not be easily modeled in a closed form analytic expression. By adopting an adversarial approach for channel response approximation and information encoding, we can jointly learn a good solution to both tasks over a wide range of channel environments. We describe the operation of the proposed adversarial system, share results for its training and validation over-the-air, and discuss implications and future work in the area.