Sensing Rate Optimization for Multi-Band Cooperative ISAC Systems

2025-03-05

AI Summary

This paper addresses the total sensing rate (SR) optimization problem in multi-band cooperative integrated sensing and communication (ISAC) systems. To overcome performance limitations of conventional equal-power allocation, we propose a linear precoding-based cross-base-station joint optimization framework. For the first time, we combine semidefinite rank relaxation (SDR) with inner approximation (IA), enabling SR maximization across multiple frequency bands and base stations via covariance matrix parameterization and semidefinite programming (SDP) modeling. The proposed method significantly enhances SR—by approximately 25% and 40% for two- and three-base-station setups, respectively—in the low transmit power regime, achieving convergence within only a few iterations. By relaxing the restrictive assumptions of band isolation and fixed per-band power allocation, our work establishes a novel paradigm for efficient multi-band ISAC resource coordination.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Applications
• Constraint Satisfaction and Optimization: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

Abstract

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies for future wireless networks, which potentially need to operate in multiple frequency bands to satisfy ever-increasing demands for both communication and sensing services. Motivated by this, we consider the sum sensing rate (SR) optimization for a cooperative ISAC system with linear precoding, where each base station (BS) works in a different frequency band. With this aim, we propose an optimization algorithm based on the semi-definite rank relaxation that introduces covariance matrices as optimization variables, and we apply the inner approximation (IA) method to deal with the nonconvexity of the resulting problem. Simulation results show that the proposed algorithm increases the SR by approximately 25 % and 40 % compared to the case of equal power distribution in a cooperative ISAC system with two and three BSs, respectively. Additionally, the algorithm converges in only a few iterations, while its most optimal implementation scenario is in the low power regime.

Problem

• Optimize sum sensing rate in multi-band ISAC systems
• Address nonconvexity using semi-definite rank relaxation
• Enhance sensing rate by 25-40% in cooperative ISAC

Innovation

• Optimizes sum sensing rate using semi-definite rank relaxation
• Applies inner approximation for nonconvex optimization problems
• Enhances sensing rate in multi-band cooperative ISAC systems