User-Centric Cell-Free Massive MIMO Enhanced by Fluid-Antenna Access Points: Uplink Analysis

2025-12-28

AI Summary

This work addresses user-centric cell-free massive MIMO systems employing access points (APs) equipped with reconfigurable fluid antennas (FAs). We propose a joint optimization framework for channel estimation, FA port selection, and uplink spectral efficiency (SE). Specifically, we design a novel FA-aware generalized linear minimum mean-square-error (LMMSE) channel estimator and develop a geometric–correlation joint analysis method for FAs based on the Jakes model. Furthermore, we introduce a distributed port selection strategy and an alternating optimization algorithm to maximize the uplink sum rate. Theoretical analysis includes uniform linear/planar array modeling, centralized maximum-ratio combining (MRC) signal-to-interference-plus-noise ratio (SINR) derivation, and the “use-and-then-forget” achievable SE bound. Simulation results demonstrate that the proposed scheme significantly reduces channel estimation error and achieves substantial uplink sum-rate gains over fixed-antenna and non-optimized FA baselines, confirming FAs as a key enabler for scalable and adaptive cell-free networks.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

In this paper, we investigate cell-free massive MIMO (CF-mMIMO) systems in which access points (APs) are equipped with fluid antennas (FAs) and develop a comprehensive framework for channel estimation, antenna port selection, and uplink spectral efficiency (SE) optimization. We propose a generalized LMMSE-based uplink channel estimation scheme that dynamically activates FA ports during pilot transmission, efficiently exploiting antenna reconfigurability under practical training constraints. Building on this, we design a distributed port selection strategy that minimizes per-AP channel estimation error by exploiting spatial correlation among FA ports. We systematically analyze the impact of antenna geometry and spatial correlation using the Jakes' channel model for different AP array configurations, including uniform linear and planar arrays. We then derive SINR expressions for centralized and distributed uplink processing and obtain a closed-form uplink SE expression for centralized maximum-ratio combining using the use-and-then-forget bound. Finally, we propose an alternating-optimization framework to select FA port configurations that maximize the uplink sum SE. Numerical results show that the proposed FA-aware channel estimation and port optimization strategies greatly reduce channel estimation error and significantly improve sum-SE over fixed-antenna and non-optimized FA baselines, confirming FAs as a key enabler for scalable, adaptive CF-mMIMO networks.

Problem

• Develops a framework for channel estimation and uplink spectral efficiency in cell-free massive MIMO with fluid antennas.
• Proposes dynamic port activation and selection strategies to minimize channel estimation error.
• Optimizes fluid antenna configurations to maximize uplink sum spectral efficiency in scalable networks.

Innovation

• Dynamic LMMSE channel estimation with fluid antenna activation
• Distributed port selection minimizing per-AP estimation error
• Alternating-optimization framework maximizing uplink sum spectral efficiency