Joint Activity Detection and Channel Estimation For Fluid Antenna System Exploiting Geographical and Angular Information

TL;DR

This paper tackles CSI acquisition and active user detection in fluid antenna systems (FAS) for massive connectivity. It introduces an approximate message passing (AMP) framework with adaptive expectation maximization (EM-AMP) and develops two variants that exploit coarse geographical and angular information to improve estimation accuracy and convergence speed while keeping computational complexity low. The authors analytically explain the performance floor observed in greedy methods and demonstrate through extensive simulations that incorporating geographical and angular priors yields substantial NMSE and ADE gains, especially in large activity regimes. The work provides theoretical insights, practical algorithms, and open-source supplementary material, demonstrating that AMP-based approaches offer scalable, flexible solutions for FAS-based communications. Practical impact includes enabling robust CSI acquisition for dense IoT scenarios with reduced pilot overhead and lower processing costs, supporting scalable deployment of FAS-enabled networks.

Abstract

The fluid antenna system (FAS) refers to a family of reconfigurable antenna technologies that provide substantial spatial gains within a compact, predefined small space, thereby offering extensive degrees of freedom in the physical layer for future communication networks. The acquisition of channel state information (CSI) is critical, as it determines the placement of ports/antennas, which directly impacts FAS-based optimization. Although various channel estimation methods have been developed, significant flaws persist. For instance, the performance of greedy-based algorithms is heavily influenced by signal assumptions, and current model-free methods are infeasible due to prohibitively high computational complexity issue. Consequently, there is a pressing need for a well-balanced solution that exhibits flexibility, feasibility, and low complexity to support massive connectivity in FAS. In this work, we propose methods based on approximate message passing (AMP) integrated with adaptive expectation maximization (EM). The EM-AMP framework uniquely enables efficient large matrix computations with adaptive learning capabilities, independent of prior knowledge of the model or parameters within potential distributions, making it a robust candidate for FAS networks. We introduce two variants of the EM-AMP framework that leverage geographical and angular features in a FAS network. These proposed algorithms demonstrate improved estimation precision, fast convergence, and low computational complexity in large activity regions. Additionally, we analytically elucidate the reasons behind the inherent performance floor of greedy-based methods and highlight the critical role of angular information in algorithm design. Extensive numerical results validate the promising efficacy of the proposed algorithm designs and the derived analytical findings.

Figures (9)

• Figure 1: Illustration of system model, where the receiver is deemed as a linear array with a service area in a circle sector.
• Figure 2: Illustration of analytical/empirical user MSE (CE) under different SNR (dB) with antenna length constant $M=64$, $N_o=16$ active ports, $G=200$ pilot length and other parameters in Table \ref{['tab:system configurations']}. Perfect priors (AD and AoAs are known) are assumed to generate analytical results by \ref{['eq:add-28']}, \ref{['eq:add-29']} and empirical results are generated by $\tilde{\mathbf{h}}= \left(\mathbf{a}^{\mathrm{H}}\mathbf{a}\right)^{-1}\mathbf{a}^{\mathrm{H}}\mathbf{Y}$ and $\tilde{\boldsymbol{\sigma}}=\mathbf{a}^{\mathrm{H}}\mathbf{Y}\mathbf{U}^{\mathrm{H}}\left(\mathbf{U}\mathbf{U}^{\mathrm{H}}\right)^{-1}$ respectively for no and with angular information.
• Figure 3: Verification on analytical results in \ref{['eq:9']} under different active ports number $N_o$ with antenna length constant $M=64$, pilot length $G=200$, different active users $K_a\in\{20,50\}$ and $\mathrm{SNR}=-10$ dB. Rician factors are $K_r=0$ (NLOS). Performance baselines in comparison are conventional EM-AMP, theoretical performance of conventional EM-AMP and analytical lower bound in \ref{['eq:9']}.
• Figure 4: Illustration of NMSE (CE) under different Rician factor $K_r$ with $M=64$ antenna length constant, $N_o=16$ active ports, $K_a=10$ active users and $\mathrm{SNR}=-10$ dB. Performance baselines in comparison are conventional EM-AMP and SOMP.
• Figure 5: Illustration of convergence behavior of different algorithms with antenna length constant $M=64$, active ports num $N_o=16$, pilot length $G=200$, $K_a=10$ active users and $\mathrm{SNR}=-14$ dB. Rician factors are $K_r=0$ (NLOS) and $K_r=4$ (LOS/NLOS). Performance baselines in comparison are conventional EM-AMP and SOMP.