Dual-Robust Integrated Sensing and Communication: Beamforming under CSI Imperfection and Location Uncertainty

TL;DR

This paper introduces a dual-robust beamforming framework for integrated sensing and communication systems that accounts for both CSI imperfections and target location uncertainty, improving performance through a novel joint optimization approach.

Contribution

It proposes a new joint robust optimization framework and an efficient two-layer algorithm to enhance ISAC system performance under practical uncertainties.

Findings

The dual-robust algorithm outperforms existing methods.

The approach effectively balances communication and sensing trade-offs.

Numerical results confirm improved robustness and efficiency.

Abstract

A dual-robust design of beamforming is investigated in an integrated sensing and communication (ISAC) system.Existing research on robust ISAC waveform design, while proposing solutions to imperfect channel state information (CSI), generally depends on prior knowledge of the target's approximate location to design waveforms. This approach, however, limits the precision in sensing the target's exact location. In this paper, considering both CSI imperfection and target location uncertainty, a novel framework of joint robust optimization is proposed by maximizing the weighted sum of worst-case data rate and beampattern gain. To address this challenging problem, we propose an efficient two-layer iteration algorithm based on S-Procedure and convex hull. Finally, numerical results verify the effectiveness and performance improvement of our dual-robust algorithm, as well as the trade-off between communication and sensing performance.