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.
@article{arxiv.2111.02106,
title = {End-to-End Learning for Integrated Sensing and Communication},
author = {José Miguel Mateos-Ramos and Jinxiang Song and Yibo Wu and Christian Häger and Musa Furkan Keskin and Vijaya Yajnanarayana and Henk Wymeersch},
journal= {arXiv preprint arXiv:2111.02106},
year = {2021}
}