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.
Neur al networks-enabled channel reconstruction for fluid antenna systems: A data-dr iven approach
2 Pith papers cite this work. Polarity classification is still indexing.
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Critiques MIMO-style channel estimation in fluid antenna systems as mismatched to selection-based operation and identifies four myths plus four open questions for practical FAMA.
citing papers explorer
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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.
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Channel Estimation and Reconstruction in Fluid Antenna Multiple Access: Myths, Misconceptions and Critical Questions
Critiques MIMO-style channel estimation in fluid antenna systems as mismatched to selection-based operation and identifies four myths plus four open questions for practical FAMA.