Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication under Hardware Impairments

TL;DR

This paper introduces a model-based end-to-end learning framework for integrated sensing and communication that effectively mitigates hardware impairments, improving detection, positioning, and symbol accuracy in ISAC systems.

Contribution

It proposes a differentiable OMP algorithm and two parameterization strategies for hardware impairments, advancing end-to-end learning in ISAC systems under practical hardware imperfections.

Findings

Learning hardware impairments improves detection and positioning accuracy.

Parameterizing hardware impairments outperforms dictionary learning in effectiveness.

The proposed methods achieve lower symbol error rates and higher detection probabilities.

Abstract

We study model-based end-to-end learning in the context of integrated sensing and communication (ISAC) under hardware impairments. Hardware impairments are usually addressed by means of array calibration with a focus on communication performance. However, residual impairments may exist that affect sensing performance. This paper proposes a data-driven framework for mitigating such impairments. A monostatic orthogonal frequency-division multiplexing (OFDM) sensing and multiple-input single-output (MISO) communication scenario is considered, incorporating hardware imperfections at the ISAC transceiver antenna array. We propose a novel differentiable version of the orthogonal matching pursuit (OMP) algorithm that is suitable for multi-target sensing and allows for efficient end-to-end learning of the hardware impairments. Based on the differentiable OMP, we devise two model-based parameterization strategies of the ISAC beamformer and sensing receiver to account for hardware impairments: (i) learning a dictionary of steering vectors for different angles and (ii) learning the parameterized hardware impairments. We carry out a comprehensive performance analysis of the proposed model-based learning approaches and a strong baseline consisting of least-squares beamforming, conventional OMP, and maximum-likelihood symbol detection for communication. Results show that by parameterizing the hardware impairments, learning approaches offer gains in terms of higher detection probability, position estimation accuracy, and lower symbol error rate (SER) compared to the baseline. We demonstrate that learning the parameterized hardware impairments outperforms learning a dictionary of steering vectors, also exhibiting the lowest complexity.