GOAMP achieves error-free reconstruction of sublinearly sparse signals from linear measurements when the measurement dimension exceeds a threshold matching that of Gaussian AMP, provided the non-zero support avoids a neighborhood of the origin.
Signal recovery from rand om mea- surements via orthogonal matching pursuit
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Model-based interpolation and sparse recovery are compared to a hierarchical Fourier neural operator that learns the mapping from FIM shapes to mmWave channel responses and outperforms baselines in accuracy and pilot efficiency.
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Generalized Orthogonal Approximate Message-Passing for Sublinear Sparsity
GOAMP achieves error-free reconstruction of sublinearly sparse signals from linear measurements when the measurement dimension exceeds a threshold matching that of Gaussian AMP, provided the non-zero support avoids a neighborhood of the origin.
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Channel Estimation for Flexible Intelligent Metasurfaces: From Model-Based Approaches to Neural Operators
Model-based interpolation and sparse recovery are compared to a hierarchical Fourier neural operator that learns the mapping from FIM shapes to mmWave channel responses and outperforms baselines in accuracy and pilot efficiency.