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Fundamental Limits of Communication-Assisted Sensing in ISAC Systems

Fuwang Dong, Fan Liu, Shihang Liu, Yifeng Xiong, Weijie Yuan, Yuanhao Cui

TL;DR

The paper develops a communication-assisted sensing (CAS) framework for ISAC systems and analyzes its fundamental limits using rate-distortion theory. It proves that, under a separable distortion metric, the end-to-end sensing QoS D = D_s + D_c is achievable if and only if the information-theoretic capacity constrained by sensing and resource costs satisfies R^{IT}(D_c) \\le C^{IT}(D_s,B); both a converse and an achievability construction are provided. An illustrative TRM-estimation example with Gaussian signaling demonstrates how D_s and D_c trade off under a power constraint, and it reveals that ISAC signaling can outperform separated sensing and communication at low SNR due to multiplexing gains, while separated schemes may dominate at high SNR. The work delivers a principled design framework for ISAC waveform optimization, guiding practical deployments of 6G-era sensing systems that exploit joint sensing-communication coordination.

Abstract

In this paper, we introduce a novel communication-assisted sensing (CAS) framework that explores the potential coordination gains offered by the integrated sensing and communication technique. The CAS system endows users with beyond-line-of-the-sight sensing capabilities, supported by a dual-functional base station that enables simultaneous sensing and communication. To delve into the system's fundamental limits, we characterize the information-theoretic framework of the CAS system in terms of rate-distortion theory. We reveal the achievable overall distortion between the target's state and the reconstructions at the end-user, referred to as the sensing quality of service, within a special case where the distortion metric is separable for sensing and communication processes. As a case study, we employ a typical application to demonstrate distortion minimization under the ISAC signaling strategy, showcasing the potential of CAS in enhancing sensing capabilities.

Fundamental Limits of Communication-Assisted Sensing in ISAC Systems

TL;DR

The paper develops a communication-assisted sensing (CAS) framework for ISAC systems and analyzes its fundamental limits using rate-distortion theory. It proves that, under a separable distortion metric, the end-to-end sensing QoS D = D_s + D_c is achievable if and only if the information-theoretic capacity constrained by sensing and resource costs satisfies R^{IT}(D_c) \\le C^{IT}(D_s,B); both a converse and an achievability construction are provided. An illustrative TRM-estimation example with Gaussian signaling demonstrates how D_s and D_c trade off under a power constraint, and it reveals that ISAC signaling can outperform separated sensing and communication at low SNR due to multiplexing gains, while separated schemes may dominate at high SNR. The work delivers a principled design framework for ISAC waveform optimization, guiding practical deployments of 6G-era sensing systems that exploit joint sensing-communication coordination.

Abstract

In this paper, we introduce a novel communication-assisted sensing (CAS) framework that explores the potential coordination gains offered by the integrated sensing and communication technique. The CAS system endows users with beyond-line-of-the-sight sensing capabilities, supported by a dual-functional base station that enables simultaneous sensing and communication. To delve into the system's fundamental limits, we characterize the information-theoretic framework of the CAS system in terms of rate-distortion theory. We reveal the achievable overall distortion between the target's state and the reconstructions at the end-user, referred to as the sensing quality of service, within a special case where the distortion metric is separable for sensing and communication processes. As a case study, we employ a typical application to demonstrate distortion minimization under the ISAC signaling strategy, showcasing the potential of CAS in enhancing sensing capabilities.
Paper Structure (13 sections, 30 equations, 3 figures)

This paper contains 13 sections, 30 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Background and Motivations
  3. System Model
  4. CAS Process
  5. Sensing Estimator and Constrained Capacity
  6. Main Results
  7. Converse
  8. Achievability
  9. Example
  10. Sensing Process
  11. Communication Process
  12. Numerical Results
  13. Conclusion

Figures (3)

  • Figure 1: The applications of the proposed novel CAS system.
  • Figure 2: The information-theoretic framework for the CAS systems.
  • Figure 3: The comparison of the sw and ISAC schemes.