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

2025-12-17

AI Summary

To address the challenges of tightly coupled user activity detection and channel estimation, strong reliance on high-SNR assumptions, and high computational complexity in fluid antenna systems (FAS) under high-dimensional sparse channels, this paper proposes an adaptive EM-AMP framework integrating geographic and angular priors. Unlike conventional model-driven approaches, the method avoids pre-specified channel models; instead, it jointly exploits angular-domain parameterization and geometric modeling to characterize spatial sparsity, while adaptively learning hyperparameters within the AMP iterations. Theoretically, we establish that angular information fundamentally determines the Cramér–Rao lower bound of estimation performance. Experiments demonstrate that, particularly under large active regions, the proposed method achieves significantly improved estimation accuracy, converges in fewer than ten iterations, and reduces computational complexity by an order of magnitude—outperforming both state-of-the-art model-based and model-free methods across all metrics.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Active Learning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Develops joint activity detection and channel estimation for fluid antenna systems
• Addresses high complexity and inflexibility in existing FAS channel estimation methods
• Proposes EM-AMP framework leveraging geographical and angular information for efficiency

Innovation

• EM-AMP framework for efficient large matrix computations
• Leveraging geographical and angular features in FAS networks
• Adaptive learning without prior knowledge of model parameters