FAS channels are represented as AR(p) Gauss-Markov processes to derive the optimal MMSE interpolator, a tight lower bound on required observations, and a Kalman filter achieving that optimum with O(N) complexity.
Finite-Aperture Fluid Antenna Array Design: Analysis and Algorithm
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abstract
Finite-aperture constraints render array design nontrivial and can undermine the effectiveness of classical sparse geometries. This letter provides universal guidance for fluid antenna array (FAA) design under a fixed aperture. We derive a closed-form Cram\'er--Rao bound (CRB) that unifies conventional and reconfigurable arrays by explicitly linking the Fisher information to the geometric variance of port locations. We further obtain a closed-form probability density function of the minimum spacing under random FAA placement, which yields a principled lower bound for the minimum-spacing constraint. Building upon these analytical insights, we then propose a gradient-based algorithm to optimize continuous port locations. Utilizing a simple gradient update design, the optimized FAA can achieve about a $30\%$ CRB reduction and a $42.5\%$ reduction in mean-squared error.
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cs.IT 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Beyond Covariance: Generative Spatial Correlation Modeling and Channel Interpolation for Fluid Antenna Systems
FAS channels are represented as AR(p) Gauss-Markov processes to derive the optimal MMSE interpolator, a tight lower bound on required observations, and a Kalman filter achieving that optimum with O(N) complexity.