Fluid Antenna-Assisted Near-Field System

TL;DR

This paper introduces a fluid antenna-assisted near-field system leveraging XL-STARS for integrated sensing and communications, optimizing multiple parameters to minimize sensing error while maintaining communication quality.

Contribution

It proposes a novel joint optimization framework for fluid antennas and XL-STARS in near-field ISAC systems, with a new iterative algorithm to solve the complex non-convex problem.

Findings

The proposed algorithm effectively reduces the CRB for sensing.

Simulation results show improved system performance.

The method balances sensing accuracy and communication quality.

Abstract

This paper proposes a fluid antenna (FA)-assisted near-field integrated sensing and communications (ISAC) system enabled by the extremely large-scale simultaneously transmitting and reflecting surface (XL-STARS). By optimizing the communication beamformer, the sensing signal covariance matrix, the XL-STARS phase shift, and the FA position vector, the Cram\'er-Rao bound (CRB), as a metric for sensing performance, is minimized while ensuring the standard communication performance. A double-loop iterative algorithm based on the penalty dual decomposition (PDD) and block coordinate descent (BCD) methods is proposed to solve the non-convex minimization problem by decomposing it into three subproblems and optimizing the coupling variables for each subproblem iteratively. Simulation results validate the superior performance of the proposed algorithm.