Model-Driven End-to-End Learning for Integrated Sensing and Communication

TL;DR

This paper introduces a model-driven learning architecture for integrated sensing and communication in 6G, demonstrating superior performance under hardware impairments and complexity constraints compared to standard neural networks.

Contribution

It proposes a novel model-driven learning approach for joint sensing and communication, outperforming neural networks especially under hardware impairments and complexity limitations.

Findings

Model-driven learning outperforms neural networks under hardware impairments.

Both learning methods surpass standard model-based approaches.

Model-driven learning generalizes better to unseen scenarios.

Abstract

Integrated sensing and communication (ISAC) is envisioned to be one of the pillars of 6G. However, 6G is also expected to be severely affected by hardware impairments. Under such impairments, standard model-based approaches might fail if they do not capture the underlying reality. To this end, data-driven methods are an alternative to deal with cases where imperfections cannot be easily modeled. In this paper, we propose a model-driven learning architecture for joint single-target multi-input multi-output (MIMO) sensing and multi-input single-output (MISO) communication. We compare it with a standard neural network approach under complexity constraints. Results show that under hardware impairments, both learning methods yield better results than the model-based standard baseline. If complexity constraints are further introduced, model-driven learning outperforms the neural-network-based approach. Model-driven learning also shows better generalization performance for new unseen testing scenarios.