SCNR Maximization for MIMO ISAC Assisted by Fluid Antenna System
Yuqi Ye, Li You, Hao Xu, Ahmed Elzanaty, Kai-Kit Wong, Xiqi Gao
TL;DR
This work tackles maximizing radar SCNR in a MIMO ISAC system augmented by a Fluid Antenna System (FAS) under per-user SINR and fluid-antenna placement constraints. An alternating-optimization framework is developed, where transmit beamforming is refined via a majorization-minimization (MM) surrogate of SCNR and antenna positions are updated iteratively through MM within an AO loop. The approach is proven to converge, and simulations show that the FAS-assisted scheme achieves higher SCNR than fixed-position baselines across practical region sizes and power budgets, highlighting the potential of dynamic antenna configurations for enhanced sensing without sacrificing communications. The results suggest meaningful gains for 6G-era ISAC deployments by leveraging fluid-antenna adaptability to improve angle resolution and interference mitigation while maintaining QoS for communications.
Abstract
The integrated sensing and communication (ISAC) technology has been extensively researched to enhance communication rates and radar sensing capabilities. Additionally, a new technology known as fluid antenna system (FAS) has recently been proposed to obtain higher communication rates for future wireless networks by dynamically altering the antenna position to obtain a more favorable channel condition. The application of the FAS technology in ISAC scenarios holds significant research potential. In this paper, we investigate a FAS-assisted multiple-input multiple-output (MIMO) ISAC system for maximizing the radar sensing signal-clutter-noise ratio (SCNR) under communication signal-to-interference-plus-noise ratio (SINR) and antenna position constraints. We devise an iterative algorithm that tackles the optimization problem by maximizing a lower bound of SCNR with respect to the transmit precoding matrix and the antenna position. By addressing the non-convexity of the problem through this iterative approach, our method significantly improves the SCNR. Our simulation results demonstrate that the proposed scheme achieves a higher SCNR compared to the baselines.