5 accepted papers
In this paper, we study the Kronecker compressed sensing problem, which focuses on recovering sparse vectors using linear measurements obtained using the Kronecker product of two or more matrices. We first introduce the hierarchical view of the Kronecker compressed sensing, showing that the Kronecke…
We study the recovery of a sparse vector with a Kronecker structure from an underdetermined linear system with a Kronecker-structured dictionary. This problem arises in several applications, such as the channel estimation of an intelligent reflecting surface-aided wireless system. Existing work only…
In this work, we study the restricted isometry property (RIP) of Kronecker-structured matrices, formed by the Kronecker product of two factor matrices. Previously, only upper and lower bounds on the restricted isometry constant (RIC) in terms of the RICs of the factor matrices were known. We derive…
This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem wi…
Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task. However, these CNN-based algorithms generally have limitations in perceiving global temporal dependencies of EEG signals. Besides, they als…