Location Optimization for Fluid Antenna-Assisted Near-Field System

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 Cramér-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.