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08/07/2023
Off-the-grid Blind Deconvolution and Demixing
We consider the problem of gridless blind deconvolution and demixing (GB...
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01/09/2023
Multi-User Distributed Computing Via Compressed Sensing
The multi-user linearly-separable distributed computing problem is consi...
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12/27/2022
Distribution-aware ℓ_1 Analysis Minimization
This work is about recovering an analysis-sparse vector, i.e. sparse vec...
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05/14/2022
Blind Goal-Oriented Massive Access for Future Wireless Networks
Emerging communication networks are envisioned to support massive wirele...
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10/30/2021
Multi-weight Matrix Completion with Arbitrary Subspace Prior Information
Matrix completion refers to completing a low-rank matrix from a few obse...
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08/21/2021
Active User Detection and Channel Estimation for Spatial-based Random Access in Crowded Massive MIMO Systems via Blind Super-resolution
This work presents a novel framework for random access in crowded scenar...
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08/14/2020
On the Sample Complexity of Super-Resolution Radar
We point out an issue with Lemma 8.6 of [1]. This lemma specifies the re...
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05/21/2020
Blind Two-Dimensional Super Resolution in Multiple Input Single Output Linear Systems
In this paper, we consider a multiple-input single-output (MISO) linear ...
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05/21/2020
Multi-weight Nuclear Norm Minimization for Low-rank Matrix Recovery in Presence of Subspace Prior Information
Weighted nuclear norm minimization has been recently recognized as a tec...
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10/30/2019
Off-the-grid Recovery of Time and Frequency Shifts with Multiple Measurement Vectors
We address the problem of estimating time and frequency shifts of a know...
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09/08/2019
Living near the edge: A lower-bound on the phase transition of total variation minimization
This work is about the total variation (TV) minimization which is used f...
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07/27/2019
A Greedy Algorithm for Matrix Recovery with Subspace Prior Information
Matrix recovery is the problem of recovering a low-rank matrix from a fe...
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09/27/2018
Optimal Exploitation of Subspace Prior Information in Matrix Sensing
Matrix sensing is the problem of reconstructing a low-rank matrix from a...
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09/09/2018
Distribution-aware Block-sparse Recovery via Convex Optimization
We study the problem of reconstructing a block-sparse signal from compre...
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08/13/2018
Improved Recovery of Analysis Sparse Vectors in Presence of Prior Information
In this work, we consider the problem of recovering analysis-sparse sign...
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06/27/2018
On the Error in Phase Transition Computations for Compressed Sensing
Evaluating the statistical dimension is a common tool to determine the a...
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04/20/2018
Exploiting Prior Information in Block Sparse Signals
We study the problem of recovering a block-sparse signal from under-samp...
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03/08/2018