Continuous Fluid Antenna Sampling for Channel Estimation in Cell-Free Massive MIMO

2026-02-18

AI Summary

This work addresses the limitation of uplink channel estimation accuracy in cell-free massive MIMO systems imposed by discrete antenna port sampling. To overcome this, the authors propose a continuous fluid antenna sampling framework, modeling the wireless channel as a spatially correlated Gaussian random field and formulating channel estimation as a motion-constrained spatial sampling problem within Gaussian process regression. Based on this formulation, they derive a linear minimum mean square error (LMMSE) estimator along with a closed-form expression for its estimation error. Theoretical analysis and numerical experiments demonstrate that, under identical spatial constraints and any finite pilot overhead, the proposed approach strictly outperforms conventional discrete-port architectures in non-degenerate spatial correlation models, achieving significantly reduced channel estimation error.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Learning with Manifolds

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements

Abstract

In this letter, we develop a continuous fluid antenna (FA) framework for uplink channel estimation in cell-free massive multiple-input and multiple-output (CF-mMIMO) systems. By modeling the wireless channel as a spatially correlated Gaussian random field, channel estimation is formulated as a Gaussian process (GP) regression problem with motion-constrained spatial sampling. Closed-form expressions for the linear minimum mean squared error (LMMSE) estimator and the corresponding estimation error are derived. A fundamental comparison with discrete port-based architectures is established under identical position constraints, showing that continuous FA sampling achieves equal or lower estimation error for any finite pilot budget, with strict improvement for non-degenerate spatial correlation models. Numerical results validate the analysis and show the performance gains of continuous FA sampling over discrete baselines.

Problem

• channel estimation
• cell-free massive MIMO
• fluid antenna
• spatial sampling
• Gaussian process

Innovation

• Continuous Fluid Antenna
• Cell-Free Massive MIMO
• Gaussian Process Regression
• Channel Estimation
• Spatial Sampling