CRB-Rate Tradeoff in RSMA-enabled Near-Field Integrated Multi-Target Sensing and Multi-User Communications
Jiasi Zhou, Cong Zhou, Yanjing Sun, and Chintha Tellambura

TL;DR
This paper investigates the tradeoff between CRB and rate in near-field RSMA-enabled ISAC systems with hybrid beamforming, proposing algorithms to optimize performance and demonstrating their effectiveness through simulations.
Contribution
It introduces a CRB-rate region analysis for near-field ISAC with RSMA and hybrid beamforming, along with novel algorithms for Pareto boundary characterization.
Findings
Achieves near-digital beamforming performance with fewer RF chains.
Outperforms space division multiple access and far-field ISAC.
Enhances CRB-rate trade-off in near-field multi-target sensing and communication.
Abstract
Extremely large-scale antenna arrays enhance spectral efficiency and spatial resolution in integrated sensing and communication (ISAC) networks while expanding the Rayleigh distance, triggering a shift from conventional far-field plane waves to near-field (NF) spherical waves. However, full-digital beamforming is infeasible due to the need for dedicated radio frequency (RF) chains. To address this, NF-ISAC with a rate-splitting multiple access (RSMA) scheme is developed for advanced interference management, considering fully-connected and partially-connected hybrid analog and digital (HAD) beamforming architectures. Specifically, the Cram\'{e}r-Rao bound (CRB) for joint distance and angle sensing is derived, and the achievable performance region between the max-min communication rate and the multi-target CRB is defined. To fully characterize the Pareto boundary of the CRB-rate region, a…
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
TopicsAdvanced Wireless Communication Technologies · Distributed Sensor Networks and Detection Algorithms · Cognitive Radio Networks and Spectrum Sensing
