Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
A systematic review of compressive sensing: Concepts, implementations and applications
Compressive Sensing (CS) is a new sensing modality, which compresses the signal being
acquired at the time of sensing. Signals can have sparse or compressible representation …
acquired at the time of sensing. Signals can have sparse or compressible representation …
Sparse synthetic aperture radar imaging from compressed sensing and machine learning: Theories, applications, and trends
Best subset selection via a modern optimization lens
D Bertsimas, A King, R Mazumder - 2016 - projecteuclid.org
Best subset selection via a modern optimization lens Page 1 The Annals of Statistics 2016, Vol.
44, No. 2, 813–852 DOI: 10.1214/15-AOS1388 © Institute of Mathematical Statistics, 2016 …
44, No. 2, 813–852 DOI: 10.1214/15-AOS1388 © Institute of Mathematical Statistics, 2016 …
[LIBRO][B] An invitation to compressive sensing
This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …
standard compressive problem studied throughout the book and reveals its ubiquity in many …
A coded compressed sensing scheme for unsourced multiple access
This article introduces a novel scheme, termed coded compressed sensing, for unsourced
multiple-access communication. The proposed divide-and-conquer approach leverages …
multiple-access communication. The proposed divide-and-conquer approach leverages …
A deep learning approach to structured signal recovery
In this paper, we develop a new framework for sensing and recovering structured signals. In
contrast to compressive sensing (CS) systems that employ linear measurements, sparse …
contrast to compressive sensing (CS) systems that employ linear measurements, sparse …
A review of sparse recovery algorithms
EC Marques, N Maciel, L Naviner, H Cai, J Yang - IEEE access, 2018 - ieeexplore.ieee.org
Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …
high-power processing, large memory density, and increased energy consumption. In …
Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds
In recent years, unfolding iterative algorithms as neural networks has become an empirical
success in solving sparse recovery problems. However, its theoretical understanding is still …
success in solving sparse recovery problems. However, its theoretical understanding is still …