End-to-End Learning for Integrated Sensing and Communication

TL;DR

This paper introduces a novel data-driven auto-encoder approach for integrated sensing and communication, effectively handling hardware impairments and model limitations to improve ISAC performance.

Contribution

It proposes a new auto-encoder architecture with a specialized loss function and training method tailored for ISAC, addressing hardware limitations and model deficits.

Findings

AE outperforms traditional methods under hardware impairments

Proposed loss function enhances ISAC robustness

Training procedure improves performance in realistic scenarios

Abstract

Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments.