Integrated Sensing and Communication for Vehicular Networks: A Rate-Distortion Fundamental Limits of State Estimator

2025-09-18

AI Summary

A fundamental gap exists in the quantitative information-theoretic analysis of sensing performance in integrated sensing and communication (ISAC) systems for vehicular networks. Method: This paper introduces, for the first time, a state-dependent memoryless channel (SDMC) model to formulate the sensing rate–distortion function. It establishes a unified “capacity–rate–distortion” trade-off region that jointly characterizes communication and sensing. Echo signals are modeled as SDMC outputs incorporating channel state information; the sensing rate–distortion function is rigorously derived, and an enhanced Blahut–Arimoto algorithm is proposed with formal convergence proof. Contribution/Results: Numerical evaluations under typical vehicular channels demonstrate that joint coding significantly improves state estimation rate. The framework reveals the fundamental theoretical gains and performance limits of ISAC co-design, providing a rigorous information-theoretic foundation for vehicular ISAC system analysis and optimization.

Technology Category

• Machine Learning: Information Theory
• 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: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The state-dependent memoryless channel (SDMC) is employed to model the integrated sensing and communication (ISAC) system for connected vehicular networks, where the transmitter conveys messages to the receiver while simultaneously estimating the state parameter of interest via the received echo signals. However, the performance of sensing has often been neglected in existing works. To address this gap, we establish the rate-distortion function for sensing performance in the SDMC model, which is defined based on standard information-theoretic principles to ensure clear operational meaning. In addition, we propose a modified Blahut-Arimoto type algorithm for solving the rate-distortion function and provide convergence proofs for the algorithm. We further define the capacity-rate-distortion tradeoff region, which, for the first time, unifies information-theoretic results for communication and sensing within a single optimization framework. Finally, we numerically evaluate the capacity-rate-distortion region and demonstrate the benefit of coding in terms of estimation rate for certain channels.

Problem

• Establishing rate-distortion function for vehicular sensing performance
• Proposing algorithm to solve capacity-rate-distortion tradeoff
• Unifying communication and sensing in optimization framework

Innovation

• Rate-distortion function for sensing performance
• Modified Blahut-Arimoto algorithm with convergence proofs
• Capacity-rate-distortion tradeoff region unifying communication and sensing