Relative Phase Equivariant Deep Neural Systems for Physical Layer Communications

TL;DR

This paper introduces a phase-equivariant deep neural network for physical layer communications, significantly improving error rates by incorporating inductive biases related to phase in the signal processing.

Contribution

It presents a novel group-equivariant neural network architecture that leverages phase symmetry, enhancing parameter efficiency and performance in communication systems.

Findings

Reduced error rates with phase-equivariant models

Improved parameter efficiency over traditional neural receivers

Potential for more energy-efficient communication systems

Abstract

In the era of telecommunications, the increasing demand for complex and specialized communication systems has led to a focus on improving physical layer communications. Artificial intelligence (AI) has emerged as a promising solution avenue for doing so. Deep neural receivers have already shown significant promise in improving the performance of communications systems. However, a major challenge lies in developing deep neural receivers that match the energy efficiency and speed of traditional receivers. This work investigates the incorporation of inductive biases in the physical layer using group-equivariant deep learning to improve the parameter efficiency of deep neural receivers. We do so by constructing a deep neural receiver that is equivariant with respect to the phase of arrival. We show that the inclusion of relative phase equivariance significantly reduces the error rate of deep neural receivers at similar model sizes. Thus, we show the potential of group-equivariant deep learning in the domain of physical layer communications.