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arXiv · 2509.12348

FAS-ARIS: Turning Multipath Challenges Into Localization Opportunities

Hua Chen·Tao Gong·Tuo Wu·Maged Elkashlan·Baiyang Liu·Chan-Byoung Chae·Kin-Fai Tong·Kai-Kit Wong
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Abstract

Traditional single-input single-output (SISO) systems face fundamental limitations in achieving accurate three-dimensional (3D) localization due to limited spatial degrees of freedom (DoF) and the adverse impact of multipath propagation. This paper proposes a novel fluid antenna system (FAS)-active reconfigurable intelligent surface (ARIS) framework that transforms multipath effects from a hindrance into a resource for enhanced localization. By synergistically combining the signal amplification capabilities of ARIS with the spatial diversity enabled by FAS, the proposed system achieves robust 3D user equipment (UE) positioning -- without relying on auxiliary information such as time-of-arrival (ToA) or frequency diversity. The system exploits both line-of-sight (LoS) and non-line-of-sight (NLoS) components through a tailored signal decoupling strategy. We design novel UE pilot sequences and ARIS phase configurations to effectively separate LoS and NLoS channels, enabling independent parameter estimation. A multi-stage estimation algorithm is then applied: the multiple signal classification (MUSIC) algorithm estimates angle-of-arrival (AoA) from the direct path, while maximum likelihood estimation with interior-point refinement recovers cascaded channel parameters from the reflected path. Finally, geometric triangulation using least-squares estimation determines the UE's 3D position based on the extracted AoA information. Comprehensive performance analysis, including the derivation of Cram\'{e}r-Rao bounds for both channel and position estimation, establishes theoretical benchmarks. Simulation results confirm that the proposed FAS-ARIS framework achieves near-optimal localization accuracy while maintaining robustness in rich multipath environments -- effectively turning conventional localization challenges into advantages.

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Hua Chen, Tao Gong, Tuo Wu, Maged Elkashlan, Baiyang Liu, Chan-Byoung Chae, Kin-Fai Tong, Kai-Kit Wong. 2025-09-15. FAS-ARIS: Turning Multipath Challenges Into Localization Opportunities. https://arxiv.org/abs/2509.12348

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