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Performance Analysis of Fluid Antenna System under Spatially-Correlated Rician Fading Channels

Information Theory 2025-05-22 v1 Signal Processing math.IT

Abstract

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. Unlike traditional fixed-position multiple-input multiple-output (MIMO) systems, a FAS possesses position reconfigurability to switch on-demand among NN predefined ports over a prescribed space. This paper explores the performance of a single-input single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closed-form bounds for the outage probability of the Rx-SISO-FAS model, as well as exact and closed-form lower bounds for the ergodic rate. Importantly, we also analyze the performance considering both uniform linear array (ULA) and uniform planar array (UPA) configurations for the ports of the FAS. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports, NN, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that: i)i) the Rx-SISO-FAS model can enhance performance under spatially-correlated Rician fading channels over the fixed-position antenna counterpart; ii)ii) the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime; iiiiii) FAS can outperform an LL branches maximum ratio combining (MRC) system under Rician fading channels; and iv)iv) when the number of ports is identical, UPA outperforms ULA.

Keywords

Cite

@article{arxiv.2505.15200,
  title  = {Performance Analysis of Fluid Antenna System under Spatially-Correlated Rician Fading Channels},
  author = {Jiangsheng Huangfu and Zhengyu Song and Tianwei Hou and Anna Li and Yuanwei Liu and Arumugam Nallanathan and Kai-Kit Wong},
  journal= {arXiv preprint arXiv:2505.15200},
  year   = {2025}
}