5 papers
Direct and Converse Theorems in Estimating Signals with Sublinear Sparsity
Keigo Takeuchi
This paper addresses the estimation of signals with sublinear sparsity sent over the additive white Gaussian noise channel. This fundamental problem arises in designing denoisers u…
Generalized Orthogonal Approximate Message-Passing for Sublinear Sparsity
Keigo Takeuchi
This paper addresses the reconstruction of sparse signals from generalized linear measurements. Signal sparsity is assumed to be sublinear in the signal dimension while it was prop…
Generalized Approximate Message-Passing for Compressed Sensing with Sublinear Sparsity
Keigo Takeuchi
This paper addresses the reconstruction of an unknown signal vector with sublinear sparsity from generalized linear measurements. Generalized approximate message-passing (GAMP) is…
Decentralized Generalized Approximate Message-Passing for Tree-Structured Networks
Keigo Takeuchi
Decentralized generalized approximate message-passing (GAMP) is proposed for compressed sensing from distributed generalized linear measurements in a tree-structured network. Conse…
Orthogonal Approximate Message-Passing for Spatially Coupled Linear Models
Keigo Takeuchi
Orthogonal approximate message-passing (OAMP) is proposed for signal recovery from right-orthogonally invariant linear measurements with spatial coupling. Conventional state evolut…