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Sensory Data Sharing in ISAC System: Fundamental Limits and Waveform Design

Fuwang Dong, Fan Liu, Yifeng Xiong, Yuanhao Cui, Wei Wang, Shi Jin

TL;DR

This work formulates a rigorous information-theoretic framework for communication-assisted sensing (CAS) within ISAC, introducing a modified source-channel separation theorem (MSST) that links distortion, rate, and constrained capacity under a separable sensing/communication distortion metric. It then develops a two-step Blahut-Arimoto approach to optimize the ISAC channel input distribution and introduces a successive convex approximation (SCA) method to design optimal Gaussian waveforms for MIMO CAS systems. The results reveal a fundamental S&C tradeoff and demonstrate that CAS-optimal signaling can interpolate between sensing- and communication-focused designs, achieving improved overall distortion performance. The practical impact lies in providing algorithmic tools for input distribution and covariance design that can be leveraged to balance sensing accuracy and data throughput in next-generation CAS-enabled networks.

Abstract

The simultaneous acquisition and sharing of sensory data through a dual-functional signaling strategy termed the communication-assisted sensing (CAS) system in this paper, has the potential to provide users with beyond-line-of-sight sensing capabilities. We mainly focus on three primary aspects, namely, the information-theoretic framework, the optimal distribution of channel input, and the optimal waveform design for Gaussian signals. First, we establish the information-theoretic framework and develop a modified source-channel separation theorem (MSST) tailored for CAS systems. The proposed MSST elucidates the relationship between achievable distortion, coding rate, and communication channel capacity in cases where the distortion metric is separable for sensing and communication (S&C) processes. Second, we present an optimal channel input design for dual-functional signaling, which aims to minimize CAS distortion under the constraints of the MSST and resource budget. We then conceive a two-step Blahut-Arimoto (BA)-based optimal search algorithm to numerically solve the functional optimization problem. Third, in light of the current signaling strategy, we further propose an optimal waveform design for Gaussian signaling in multi-input multi-output (MIMO) CAS systems. The associated covariance matrix optimization problem is addressed using a successive convex approximation (SCA)-based waveform design algorithm. Finally, we provide numerical simulation results to demonstrate the effectiveness of the proposed algorithms and to show the unique performance tradeoff between S&C processes.

Sensory Data Sharing in ISAC System: Fundamental Limits and Waveform Design

TL;DR

This work formulates a rigorous information-theoretic framework for communication-assisted sensing (CAS) within ISAC, introducing a modified source-channel separation theorem (MSST) that links distortion, rate, and constrained capacity under a separable sensing/communication distortion metric. It then develops a two-step Blahut-Arimoto approach to optimize the ISAC channel input distribution and introduces a successive convex approximation (SCA) method to design optimal Gaussian waveforms for MIMO CAS systems. The results reveal a fundamental S&C tradeoff and demonstrate that CAS-optimal signaling can interpolate between sensing- and communication-focused designs, achieving improved overall distortion performance. The practical impact lies in providing algorithmic tools for input distribution and covariance design that can be leveraged to balance sensing accuracy and data throughput in next-generation CAS-enabled networks.

Abstract

The simultaneous acquisition and sharing of sensory data through a dual-functional signaling strategy termed the communication-assisted sensing (CAS) system in this paper, has the potential to provide users with beyond-line-of-sight sensing capabilities. We mainly focus on three primary aspects, namely, the information-theoretic framework, the optimal distribution of channel input, and the optimal waveform design for Gaussian signals. First, we establish the information-theoretic framework and develop a modified source-channel separation theorem (MSST) tailored for CAS systems. The proposed MSST elucidates the relationship between achievable distortion, coding rate, and communication channel capacity in cases where the distortion metric is separable for sensing and communication (S&C) processes. Second, we present an optimal channel input design for dual-functional signaling, which aims to minimize CAS distortion under the constraints of the MSST and resource budget. We then conceive a two-step Blahut-Arimoto (BA)-based optimal search algorithm to numerically solve the functional optimization problem. Third, in light of the current signaling strategy, we further propose an optimal waveform design for Gaussian signaling in multi-input multi-output (MIMO) CAS systems. The associated covariance matrix optimization problem is addressed using a successive convex approximation (SCA)-based waveform design algorithm. Finally, we provide numerical simulation results to demonstrate the effectiveness of the proposed algorithms and to show the unique performance tradeoff between S&C processes.
Paper Structure (24 sections, 61 equations, 12 figures, 4 algorithms)

This paper contains 24 sections, 61 equations, 12 figures, 4 algorithms.

Table of Contents

  1. Introduction
  2. Communication-Assisted Sensing Systems
  3. Related Works
  4. Our contributions
  5. Information-theoretic framework of CAS
  6. Sensing and Communication Processes
  7. The Estimate and Compress Strategy
  8. Modified Source-Channel Separation Theorem
  9. Converse
  10. Achievability
  11. Channel Input Distribution Optimization for the CAS Systems
  12. Problem Formulation
  13. Two-step BA-based Optimal Search Algorithm
  14. Waveform Design for Gaussian Channel Input
  15. System Model
  16. ...and 9 more sections

Figures (12)

  • Figure 1: The application scenarios of the CAS system.
  • Figure 2: The comparisons of the information-theoretic frameworks between the remote estimation, remote source coding, and the proposed CAS system.
  • Figure 3: The changes of the snc distortions with various factor $\mu$.
  • Figure 4: The CAS distortion versus the communication channel capacity.
  • Figure 5: The channel input distribution of snc-optimal and CAS-optimal.
  • ...and 7 more figures