Fairness vs. Equality: RSMA-Based Multi-Target and Multi-User Integrated Sensing and Communications

2025-04-04

AI Summary

This paper addresses the joint optimization of communication rate and sensing accuracy in power-constrained integrated sensing and communication (ISAC) systems supporting multi-target sensing and multi-user communications. We propose a Pareto-optimal framework based on rate-splitting multiple access (RSMA), which is the first to jointly design fairness-aware beamforming, common-rate splitting, and sensing-power allocation to minimize the multi-target Cramér–Rao bound (CRB) while maximizing the weighted sum rate. The resulting non-convex problem is tackled via Taylor series expansion, semidefinite relaxation (SDR), successive convex approximation (SCA), and a penalty method. Compared with NOMA, SDMA, and OMA benchmarks, the proposed scheme achieves significant improvements in both sensing accuracy—evidenced by lower CRB—and communication fairness, yielding comprehensive performance gains for ISAC.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper investigates the tradeoff between sensing and communication in an ISAC system comprising multiple sensing targets and communication users. A dual-functional base station conducts downlink data transmission services based on RSMA for multiple users, while sensing surrounding multiple targets. To enable effective multicast communications and ensure fair and balanced multi-target sensing and under a constrained power budget, we propose a multi-target sensing enhancement scheme incorporating fairness-aware BF, common rate splitting, and sensing power allocation. The proposed scheme minimizes the sensing CRB, while maximizing communication rate demands. Specifically, we derive closed-form expressions for both sensing CRB and communication rates. Building upon them, we formulate an optimization problem aiming to minimize the sensing CRB, while maximizing the communication rates. Considering the non-convex nature of the original optimization problem poses significant computational challenges, we transform the tradeoff optimization into a Pareto-optimal problem by employing Taylor series expansion, semi-definite relaxation, successive convex approximation, and penalty function to transform the non-convex problem and associated constraints into tractable forms. Extensive simulations validate the theoretical analysis and demonstrate significant advantages of the proposed RSMA-based fairness-aware BF over non-orthogonal multiple access, space division multiple access, and orthogonal multiple access, through comprehensive comparisons in two key aspects: CRB performance improvement and sensing-communication tradeoff characteristics. The proposed optimization framework exhibits remarkable superiority in enhancing both sensing accuracy and communication quality for ISAC systems.

Problem

• Balancing fairness and equality in RSMA-based ISAC systems
• Optimizing sensing CRB and communication rates under power constraints
• Transforming non-convex tradeoff into Pareto-optimal solutions

Innovation

• RSMA-based fairness-aware beamforming for ISAC
• Multi-target sensing with power allocation
• Non-convex optimization via Taylor and SCA