Papers compressed sensing
“compressed sensing” 태그가 달린 논문 992편 · 필터 해제
Cross-Channel Unlabeled Sensing over a Union of Signal Subspaces
Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels. This work expands the cross-channel unlabeled sensing framework to signals …
compressed sensingNear-Field Directional Modulation for RIS-Aided Movable Antenna MIMO Systems with Hardware Impairments
Movable antennas (MAs) are a promising technology to achieve a significant enhancement in rate for future wireless networks. The pioneering investigation on near-field directional modulation design for a reconfigurable i…
compressed sensingGraph Neural Network Aided Detection for the Multi-User Multi-Dimensional Index Modulated Uplink
The concept of Compressed Sensing-aided Space-Frequency Index Modulation (CS-SFIM) is conceived for the Large-Scale Multi-User Multiple-Input Multiple-Output Uplink (LS-MU-MIMO-UL) of Next-Generation (NG) networks. Expli…
compressed sensingGraph Neural NetworkBeyond Discreteness: Finite-Sample Analysis of Straight-Through Estimator for Quantization
Training quantized neural networks requires addressing the non-differentiable and discrete nature of the underlying optimization problem. To tackle this challenge, the straight-through estimator (STE) has become the most…
compressed sensingQuantizationSimplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels
We introduce the Sparse pretrained Radio Transformer (SpaRTran), an unsupervised representation learning approach based on the concept of compressed sensing for radio channels. Our approach learns embeddings that focus o…
compressed sensingRepresentation LearningNear-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information
This paper investigates the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first es…
compressed sensingConvergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction
In compressed sensing (CS) MRI, model-based methods are pivotal to achieving accurate reconstruction. One of the main challenges in model-based methods is finding an effective prior to describe the statistical distributi…
compressed sensingDenoisingMRI ReconstructionCross-organ all-in-one parallel compressed sensing magnetic resonance imaging
Recent advances in deep learning-based parallel compressed sensing magnetic resonance imaging (p-CSMRI) have significantly improved reconstruction quality. However, current p-CSMRI methods often require training separate…
Allcompressed sensingImage ReconstructionMambaOptimal Vector Compressed Sensing Using James Stein Shrinkage
The trend in modern science and technology is to take vector measurements rather than scalars, ruthlessly scaling to ever higher dimensional vectors. For about two decades now, traditional scalar Compressed Sensing has b…
compressed sensingParametrized Stacked Intelligent Metasurfaces for Bistatic Integrated Sensing and Communications
We consider stacked intelligent metasurfaces (SIMs) as a tool to improve the performance of bistatic integrated sensing and communications (ISAC) schemes. To that end, we optimize the SIMs and design a radar parameter es…
compressed sensingISACparameter estimationDiffusion Probabilistic Models for Compressive SAR Imaging
Compressed sensing Synthetic Aperture Radar (SAR) image formation, formulated as an inverse problem and solved with traditional iterative optimization methods can be very computationally expensive. We investigate the use…
compressed sensingDenoisingImage ReconstructionModular XL-Array-Enabled 3-D Localization based on Hybrid Spherical-Planar Wave Model in Terahertz Systems
This work considers the three-dimensional (3-D) positioning problem in a Terahertz (THz) system enabled by a modular extra-large (XL) array with sub-connected architecture. Our purpose is to estimate the Cartesian Coordi…
compressed sensingPositionParameter Convergence Detector Based on VAMP Deep Unfolding: A Novel Radar Constant False Alarm Rate Detection Algorithm
The sub-Nyquist radar framework exploits the sparsity of signals, which effectively alleviates the pressure on system storage and transmission bandwidth. Compressed sensing (CS) algorithms, such as the VAMP algorithm, ar…
compressed sensingDenoising guarantees for optimized sampling schemes in compressed sensing
Compressed sensing with subsampled unitary matrices benefits from \emph{optimized} sampling schemes, which feature improved theoretical guarantees and empirical performance relative to uniform subsampling. We provide, in…
compressed sensingDenoisingPLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication
Integrated sensing and communication (ISAC) is envisioned be to one of the paradigms upon which next-generation mobile networks will be built, extending localization and tracking capabilities, as well as giving birth to …
compressed sensingIntegrated sensing and communicationISACmultidimensional harmonic retrieval+4Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers
This paper addresses the task of learning convex regularizers to guide the reconstruction of images from limited data. By imposing that the reconstruction be amplitude-equivariant, we narrow down the class of admissible …
compressed sensingDenoisingLeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models
Recent advances in Latent Video Diffusion Models (LVDMs) have revolutionized video generation by leveraging Video Variational Autoencoders (Video VAEs) to compress intricate video data into a compact latent space. Howeve…
compressed sensingVideo GenerationVideo ReconstructionJoint Beamforming and Compressed Sensing for Uplink Grant-Free Access
Compressed sensing (CS)-based techniques have been widely applied in the grant-free non-orthogonal multiple access (NOMA) to a single-antenna base station (BS). In this paper, we consider the multi-antenna reception at t…
compressed sensingPulse Processing -- Overview and Challenges
The detection of irregularly spaced pulses of non-negligible width is a fascinating yet under-explored topic in signal processing. It sits adjacent to other core topics such as radar and symbol detection yet has its own …
compressed sensingDiagnosticPositionSpike SortingAccelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations
The scanning time for a fully sampled MRI can be undesirably lengthy. Compressed sensing has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally c…
Anatomycompressed sensingGenerative Adversarial NetworkMRI Reconstruction