Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
A review of sparse recovery algorithms
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 …
Domain generalization in machine learning models for wireless communications: Concepts, state-of-the-art, and open issues
Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …
next-generation wireless systems. This led to a large body of research work that applies ML …
Channel estimation for RIS-empowered multi-user MISO wireless communications
Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-
efficient solution for future wireless networks due to their fast and low-power configuration …
efficient solution for future wireless networks due to their fast and low-power configuration …
AMP-Net: Denoising-based deep unfolding for compressive image sensing
Most compressive sensing (CS) reconstruction methods can be divided into two categories,
ie model-based methods and classical deep network methods. By unfolding the iterative …
ie model-based methods and classical deep network methods. By unfolding the iterative …
AMP-inspired deep networks for sparse linear inverse problems
Deep learning has gained great popularity due to its widespread success on many inference
problems. We consider the application of deep learning to the sparse linear inverse …
problems. We consider the application of deep learning to the sparse linear inverse …
A unifying tutorial on approximate message passing
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …
extremely popular in various structured high-dimensional statistical problems. Although the …
Grant-free massive MTC-enabled massive MIMO: A compressive sensing approach
A key challenge of massive MTC (mMTC), is the joint detection of device activity and
decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) …
decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) …
Orthogonal amp
Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for
linear system models. When the system transform matrix has independent identically …
linear system models. When the system transform matrix has independent identically …
Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery
Magnetic resonance imaging (MRI) is a noninvasive diagnostic tool that provides excellent
soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging …
soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging …
Regularization by denoising: Clarifications and new interpretations
Regularization by denoising (RED), as recently proposed by Romano, Elad, and Milanfar, is
powerful image-recovery framework that aims to minimize an explicit regularization objective …
powerful image-recovery framework that aims to minimize an explicit regularization objective …