Hybrid Mono- and Bi-static OFDM-ISAC via BS-UE Cooperation: Closed-Form CRLB and Coverage Analysis
Xiaoli Xu, Yong Zeng

TL;DR
This paper introduces a hybrid mono- and bi-static OFDM-ISAC framework leveraging BS-UE cooperation, deriving fundamental performance bounds, and analyzing coverage and accuracy improvements without extra spectrum costs.
Contribution
It presents a novel hybrid sensing scheme based on existing 3GPP modes, with closed-form CRLB derivations and coverage analysis, enhancing sensing performance in ISAC systems.
Findings
Significant performance gains over mono- or bi-static sensing in favorable geometries.
Sensing coverage varies with UE position, improving then degrading with increased BS-UE distance.
Derived sensing accuracy as a function of UE density and target location.
Abstract
This paper proposes a hybrid mono- and bi-static sensing framework, by leveraging the base station (BS) and user equipment (UE) cooperation in integrated sensing and communication (ISAC) systems. This scheme is built on 3GPP-supported sensing modes, and it does not incur any extra spectrum cost or inter-cell coordination. To reveal the fundamental performance limit of the proposed hybrid sensing mode, we derive closed-form Cram\'{e}r-Rao lower bound (CRLB) for sensing target localization and velocity estimation, as functions of target and UE positions. The results reveal that significant performance gains can be achieved over the purely mono- or bi-static sensing, especially when the BS-target-UE form a favorable geometry, which is close to a right triangle. The analytical results are validated by simulations using effective parameter estimation algorithm and weighted mean square error…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRadar Systems and Signal Processing · Distributed Sensor Networks and Detection Algorithms · Indoor and Outdoor Localization Technologies
