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

Xudong Li, Rugui Yao, Alexandros-Apostolos A. Boulogeorgos, Theodoros A. Tsiftsis

TL;DR

This work addresses the sensing-communication tradeoff in a RSMA-based ISAC system with multiple targets and users under imperfect CSI and a finite power budget. It derives closed-form expressions for sensing CRB and downlink rates, then casts a joint beamforming, sensing-power allocation, and common rate splitting problem into a Pareto-optimal form solvable by Taylor expansion, SDR, SCA, and penalties. The proposed fairness-aware RSMA framework demonstrates significant CRB reductions and favorable tradeoffs compared with NOMA, SDMA, and OMA, while maintaining or improving communication performance. The results establish RSMA as an effective mechanism to achieve balanced, robust ISAC performance in dynamic, multi-target environments, with practical implications for 6G deployments.

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.

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

TL;DR

This work addresses the sensing-communication tradeoff in a RSMA-based ISAC system with multiple targets and users under imperfect CSI and a finite power budget. It derives closed-form expressions for sensing CRB and downlink rates, then casts a joint beamforming, sensing-power allocation, and common rate splitting problem into a Pareto-optimal form solvable by Taylor expansion, SDR, SCA, and penalties. The proposed fairness-aware RSMA framework demonstrates significant CRB reductions and favorable tradeoffs compared with NOMA, SDMA, and OMA, while maintaining or improving communication performance. The results establish RSMA as an effective mechanism to achieve balanced, robust ISAC performance in dynamic, multi-target environments, with practical implications for 6G deployments.

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.

Paper Structure

This paper contains 16 sections, 1 theorem, 51 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Literature Review
  4. Motivation and Contribution
  5. Organization and Notation
  6. System and Channel Model
  7. Network topology
  8. Communication Signal Model
  9. Sensing Signal Model
  10. Optimization Formulation and Solution
  11. Optimization Formulation
  12. Optimization Solution
  13. Computation Complexity Analysis
  14. Numerical Results and Discussions
  15. Conclusion
  16. ...and 1 more sections

Key Result

Theorem 1

The optimization problem in (28) can be equivalently expressed as

Figures (7)

  • Figure 1: Considered multi-user multi-target ISAC system.
  • Figure 2: Convergence performance under different algorithms.
  • Figure 3: CRB versus CSI uncertainty for different MA schemes.
  • Figure 4: CRB versus transmit power for different sensing and MA schemes.
  • Figure 5: Pareto boundary on sensing and communications for different sensing and MA schemes.
  • ...and 2 more figures

Theorems & Definitions (2)

  • Theorem 1
  • proof