Extended AB Algorithms for Bistatic Integrated Sensing and Communications Systems

2025-08-10

AI Summary

This paper addresses the challenge of accurately characterizing the rate-distortion trade-off in bistatic integrated sensing and communication (ISAC) systems. To this end, we propose an extended Arimoto–Blahut (AB) algorithm. Our key methodological innovation is the introduction of auxiliary variables to equivalently transform the non-convex distortion constraint into a linear one, with rigorous proof that the reformulated problem shares the same optimal solution as the original. Building upon this, we develop a novel AB iterative framework tailored to both squared-error and logarithmic loss distortion measures, incorporating Lagrangian duality to ensure convergence. Numerical experiments demonstrate that the proposed algorithm efficiently and robustly computes the rate-distortion boundary for bistatic ISAC systems, significantly outperforming the conventional AB algorithm under non-convex constraints. The method thus provides a reliable analytical tool for fundamental performance limit evaluation in ISAC.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Integrated sensing and communication (ISAC) is pivotal for next-generation wireless networks, rendering the computation of rate-distortion trade-off in ISAC systems critically important. In this paper, we propose the extended Arimoto-Blahut (AB) algorithms to calculate the rate-distortion trade-off in bistatic ISAC systems, which overcome the limitation of existing AB algorithms in handling non-convex constraints. Specifically, we introduce auxiliary variables to transform non-convex distortion constraints into linear constraints, prove that the reformulated linearly-constrained optimization problem maintains the same optimal solution as the original problem, and develop extended AB algorithms for both squared error and logarithmic loss distortion metrics based on the framework of AB algorithm. Numerical results validate the effectiveness of the proposed algorithm.

Problem

• Computing rate-distortion trade-off in bistatic ISAC systems
• Overcoming non-convex constraint limitations in AB algorithms
• Transforming non-convex constraints into linear ones effectively

Innovation

• Extended AB algorithms for bistatic ISAC systems
• Transform non-convex constraints via auxiliary variables
• Develop algorithms for error and loss metrics