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An Efficient Sum-Rate Maximization Algorithm for Fluid Antenna-Assisted ISAC System

Qian Zhang, Mingjie Shao, Tong Zhang, Gaojie Chen, Ju Liu, P. C. Ching

TL;DR

This work addresses joint FA-position and waveform optimization in a fluid antenna–assisted ISAC system to maximize multiuser sum-rate under a sensing/probing constraint. It develops a block successive upper bound minimization framework combined with a proximal distance approach (PDA) for beamformer updates and an extrapolated projected gradient method (EPG) for antenna-position updates, yielding closed-form updates per iteration. The method includes efficient auxiliary-variable updates and a complexity-aware design, achieving substantial speedups compared with SCA and PSO while delivering competitive or superior sum-rate performance, especially for larger FA scales. The results demonstrate the practical viability of FA-enabled ISAC with scalable optimization, enabling higher spectral efficiency and sensing capabilities in next-generation networks.

Abstract

This letter investigates a fluid antenna (FA)-assisted integrated sensing and communication (ISAC) system, with joint antenna position optimization and waveform design. We consider enhancing the sum-rate maximization (SRM) and sensing performance with the aid of FAs. Although the introduction of FAs brings more degrees of freedom for performance optimization, its position optimization poses a non-convex programming problem and brings great computational challenges. This letter contributes to building an efficient design algorithm by the block successive upper bound minimization and majorization-minimization principles, with each step admitting closed-form update for the ISAC waveform design. In addition, the extrapolation technique is exploited further to speed up the empirical convergence of FA position design. Simulation results show that the proposed design can achieve state-of-the-art sum-rate performance with at least 60% computation cutoff compared to existing works with successive convex approximation (SCA) and particle swarm optimization (PSO) algorithms.

An Efficient Sum-Rate Maximization Algorithm for Fluid Antenna-Assisted ISAC System

TL;DR

This work addresses joint FA-position and waveform optimization in a fluid antenna–assisted ISAC system to maximize multiuser sum-rate under a sensing/probing constraint. It develops a block successive upper bound minimization framework combined with a proximal distance approach (PDA) for beamformer updates and an extrapolated projected gradient method (EPG) for antenna-position updates, yielding closed-form updates per iteration. The method includes efficient auxiliary-variable updates and a complexity-aware design, achieving substantial speedups compared with SCA and PSO while delivering competitive or superior sum-rate performance, especially for larger FA scales. The results demonstrate the practical viability of FA-enabled ISAC with scalable optimization, enabling higher spectral efficiency and sensing capabilities in next-generation networks.

Abstract

This letter investigates a fluid antenna (FA)-assisted integrated sensing and communication (ISAC) system, with joint antenna position optimization and waveform design. We consider enhancing the sum-rate maximization (SRM) and sensing performance with the aid of FAs. Although the introduction of FAs brings more degrees of freedom for performance optimization, its position optimization poses a non-convex programming problem and brings great computational challenges. This letter contributes to building an efficient design algorithm by the block successive upper bound minimization and majorization-minimization principles, with each step admitting closed-form update for the ISAC waveform design. In addition, the extrapolation technique is exploited further to speed up the empirical convergence of FA position design. Simulation results show that the proposed design can achieve state-of-the-art sum-rate performance with at least 60% computation cutoff compared to existing works with successive convex approximation (SCA) and particle swarm optimization (PSO) algorithms.
Paper Structure (8 sections, 29 equations, 5 figures, 1 algorithm)

This paper contains 8 sections, 29 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Beamformer and APV Optimization
  4. Beamformer Optimization
  5. APV Optimization
  6. Computational Complexity Analysis
  7. Simulation Results
  8. Conclusion

Figures (5)

  • Figure 1: The model of fluid antenna-assisted ISAC system.
  • Figure 2: Comparison between the proposed algorithm and existing algorithms with different $P_t$. $K=2$, $P_{max}=30\,$dBm.
  • Figure 3: Comparison between the proposed algorithm and existing algorithms with different $M$. $K=8$, $D=M\lambda$, $P_{max}=30\,$dBm, $P_t=6\,$W.
  • Figure 4: Trade-off performance between multiuser sum rate and probing power. $M=8,~P_{max}=30\,$dBm.
  • Figure 5: Multiuser sum rate under different number of BS antenna. $K=2$.