Hybrid Mono- and Bi-static OFDM-ISAC via BS-UE Cooperation: Closed-Form CRLB and Coverage Analysis

2026-01-14

AI Summary

This work addresses the performance limitations in target localization and velocity estimation within integrated sensing and communication (ISAC) systems by proposing a hybrid mono-/bi-static OFDM-ISAC sensing framework that leverages collaboration between base stations (BSs) and user equipment (UEs) without requiring additional spectrum or inter-cell coordination. The study derives, for the first time, closed-form Cramér–Rao lower bounds (CRLBs) for localization and velocity estimation under this hybrid configuration, revealing significant performance gains when the BS–target–UE geometry approximates a right triangle. Building on this insight, the paper introduces a UE-position-based sensing coverage analysis and an optimal UE selection strategy. Simulations demonstrate that the proposed approach outperforms purely mono- or bi-static schemes under favorable geometric conditions, with sensing coverage first improving and then degrading as the BS–UE distance increases, and with UE density exhibiting a quantifiable impact on estimation accuracy.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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 (MSE) fusion method. Based on the derived sensing bound, we further analyze the sensing coverage by varying the UE positions, which shows that sensing coverage first improves then degrades as the BS-UE separation increases. Furthermore, the sensing accuracy for a potential target with best UE selection is derived as a function of the UE density in the network.

Problem

• ISAC
• hybrid mono/bi-static sensing
• CRLB
• sensing coverage
• BS-UE cooperation

Innovation

• Hybrid mono/bi-static sensing
• BS-UE cooperation
• Closed-form CRLB
• ISAC coverage analysis
• OFDM-ISAC