Intelligent Reflecting Surface Enabled Multi-Target Sensing
Kaitao Meng, Qingqing Wu, Robert Schober, and Wen Chen

TL;DR
This paper explores IRS-assisted multi-target sensing, proposing novel schemes to optimize sensing performance without direct target links, including time division, signature sequence, and hybrid methods for flexible sensing strategies.
Contribution
It introduces three innovative IRS-assisted sensing schemes and joint optimization techniques to enhance multi-target sensing in challenging scenarios without direct paths.
Findings
Proposed three IRS-assisted sensing schemes: TD, SS, and hybrid TD-SS.
Joint optimization of beamforming and IRS phase shifts improves sensing performance.
Hybrid scheme offers flexible trade-offs between beam gain and sensing frequency.
Abstract
Besides improving communication performance, intelligent reflecting surfaces (IRSs) are also promising enablers for achieving larger sensing coverage and enhanced sensing quality. Nevertheless, in the absence of a direct path between the base station (BS) and the targets, multi-target sensing is generally very difficult, since IRSs are incapable of proactively transmitting sensing beams or analyzing target information. Moreover, the echoes of different targets reflected via the IRS-established virtual links share the same directionality at the BS. In this paper, we study a wireless system comprising a multi-antenna BS and an IRS for multi-target sensing, where the beamforming vector and the IRS phase shifts are jointly optimized to improve the sensing performance. To meet the different sensing requirements, such as a minimum received power and a minimum sensing frequency, we propose…
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