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

2024-09-05

AI Summary

This paper addresses the joint optimization of sensing and communication in communication-assisted sensing (CAS) systems. To this end, it proposes the first information-theoretic framework tailored for CAS: a modified source–channel separation theorem (MSST) under decoupled distortion measures, establishing— for the first time—the explicit theoretical trade-off between sensing distortion and communication rate. Furthermore, it introduces a dual-functional waveform optimization paradigm integrating block-coordinate ascent (BA) iteration with successive convex approximation (SCA), enabling joint optimal design of MIMO Gaussian input distributions and transmit waveforms. Theoretical analysis characterizes the fundamental performance limits of sensing and communication (S&C) under CAS. Simulation results demonstrate that the proposed method significantly outperforms conventional separated designs under resource constraints, achieving Pareto-optimal frontier improvement.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• Develops an information-theoretic framework for CAS systems.
• Optimizes channel input design for dual-functional signaling.
• Proposes optimal waveform design for MIMO CAS systems.

Innovation

• Developed modified source-channel separation theorem
• Designed optimal channel input for dual-functional signaling
• Proposed optimal waveform design for MIMO systems