Joint channel and impulsive noise estimation method based on approximate message passing for NOMA systems by LI Youming, MA Chongya, WU Yonghong, GUO Qiang

“…Firstly, based on sparse Bayesian learning theory, a compressed sensing equation was constructed by using all subcarriers, and then a joint estimation optimization problem for the channel, impulsive noise, and data symbols was proposed. …”
Get full text

Efficient high‐speed framework for sparse representation‐based iris recognition by Michael Melek, Mohamed F. Abu‐Elyazeed, Ahmed Khattab

“…To considerably improve classification performance, SRC is proposed, using a greedy compressed‐sensing recovery algorithm, as opposed to employing the traditional computationally expensive ℓ1 minimisation. …”
Get full text

CS-communication integration method in IoT monitoring multiple phenomena by Chengcheng HAN, Li CHEN, Weidong WANG

“…The large-scale Internet of things with multi observation phenomenon uses orthogonal multiple access (OMA) mechanism to transmit the observation data of distributed nodes, which will cause great transmission delay and lead to the loss of timeliness of data.To cope with the heavy latency of observations due to OMA, an efficient scheme integrating compressed sensing (CS) technique with communication was proposed for large-scale Internet of things to monitor multiple phenomena.In this proposed scheme, the nodes monitoring different phenomena were assigned to different time durations for transmission.During the assigned time duration, all nodes concurrently transmitted observations to the fusion center (FC)for CS measurement, and the FC recovered observation by CS algorithms.To evaluate the performance of the proposed scheme, the achievable rate of the observed phenomena was derived, which was closely related to the time allocation of clusters.To further improve the performance, the optimization problems of time allocation were studied under the two objectives of maximizing the total rate and ensuring the fairness of observation.Finally, the performance was verified and analyzed by numerical simulation.The simulation results show that the achievable rate of observations for different phenomena is improved the proposed scheme significantly compared with OMA schemes.…”
Get full text

Random Frequency Division Multiplexing by Chanzi Liu, Jianjian Wu, Qingfeng Zhou

“…In this paper, we propose a random frequency division multiplexing (RFDM) method for multicarrier modulation in mobile time-varying channels. Inspired by compressed sensing (CS) technology which use a sensing matrix (with far fewer rows than columns) to sample and compress the original sparse signal simultaneously, while there are many reconstruction algorithms that can recover the original high-dimensional signal from a small number of measurements at the receiver. …”
Get full text

Pilot Design for Sparse Channel Estimation in Large-Scale MIMO-OFDM System by Chao Xu, Jianhua Zhang, Mengmeng Liu, Changchuan Yin

“…The pilot design problem in large-scale multi-input-multioutput orthogonal frequency division multiplexing (MIMO-OFDM) system is investigated from the perspective of compressed sensing (CS). According to the CS theory, the success probability of estimation is dependent on the mutual coherence of the reconstruction matrix. …”
Get full text

Doppler Ambiguity Resolution Based on Random Sparse Probing Pulses by Yunjian Zhang, Zhenmiao Deng, Jianghong Shi, Linmei Ye, Maozhong Fu, Chen Zhao

“…A novel method for solving Doppler ambiguous problem based on compressed sensing (CS) theory is proposed in this paper. …”
Get full text

Distributed Compressed Video Sensing in Camera Sensor Networks by Yu Liu, Xuqi Zhu, Lin Zhang, Sung Ho Cho

“…In such scenarios, distributed compressed video sensing (DCVS), combining distributed video coding (DVC) and compressed sensing (CS), is developed as a novel and powerful signal-sensing and compression algorithm for video signals. …”
Get full text

Cardiac MR image reconstruction using cascaded hybrid dual domain deep learning framework. by Madiha Arshad, Faisal Najeeb, Rameesha Khawaja, Amna Ammar, Kashif Amjad, Hammad Omer

“…Techniques such as Compressed Sensing (CS) and Parallel Imaging (pMRI) have been proposed to accelerate MRI data acquisition and improve image quality. …”
Get full text

Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning by Hua Xiao, Zhongliang Wang, Xueying Cui, Liping Wang, Hongsheng Yang, Yingbiao Jia

“…Extensive experimental results on five real hyperspectral datasets demonstrate that the proposed spectral library learning, abundance initialization, and reconstruction strategy can effectively improve the compressed sensing reconstruction accuracy, outperforming the existing state-of-the-art methods.…”
Get full text

Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user m... by Shoukath Ali K, Sajan P Philip, Arfat Ahmad Khan, Leeban Moses, Korhan Cengiz, Sedat Akleylek, Nikola Ivković

“…The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). …”
Get full text

IM-OFDM ISAC Outperforms OFDM ISAC by Combining Multiple Sensing Observations by Hugo Hawkins, Chao Xu, Lie-Liang Yang, Lajos Hanzo

“…The existing solutions either insert a radar signal into the deactivated subcarriers, thereby using a radar signal for sensing, or employ compressed sensing, which leads to a lower sensing performance than OFDM ISAC. …”
Get full text

Conditional diffusion-generated super-resolution for myocardial perfusion MRI by Changyu Sun, Changyu Sun, Neha Goyal, Yu Wang, Darla L. Tharp, Senthil Kumar, Talissa A. Altes

“…While techniques like parallel imaging and compressed sensing have significantly advanced perfusion imaging, they still suffer from noise amplification, residual artifacts, and potential temporal blurring due to the rapid transit of dynamic contrast vs. the temporal constraints of the reconstruction.MethodsThis study introduces a conditional diffusion-based generative model for myocardial perfusion MRI super resolution, addressing the trade-offs between spatiotemporal resolution and slice coverage. …”
Get full text

UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review by Zhen Zhang, Lehao Huang, Qingwang Wang, Linhuan Jiang, Yemao Qi, Shunyuan Wang, Tao Shen, Bo-Hui Tang, Yanfeng Gu

“…This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. …”
Get full text

Zero-effort projection for sensory data reconstruction in wireless sensor networks by Xiancun Zhou, Haibo Ling

“…Compressive sensing is a promising technique for data gathering in large-scale wireless sensor networks. …”
Get full text

Sparse Recovery for Bistatic MIMO Radar Imaging in the Presence of Array Gain Uncertainties by Jun Li, Shengqi Zhu, Xixi Chen, Li Lv, Guisheng Liao, Menglei Yi

“…The imaging is then performed by compressive sensing method with consideration of both the transmit and receive array gain uncertainties. …”
Get full text

Mixed-Signal Parallel Compressive Spectrum Sensing for Cognitive Radios by Zhuizhuan Yu, Xi Chen, Sebastian Hoyos, Brian M. Sadler, Jingxuan Gong, Chengliang Qian

“…In this paper, a mixed-signal parallel compressive sensing architecture is developed to realize wideband spectrum sensing for cognitive radios at sub-Nqyuist rates by exploiting the sparsity in current frequency usage. …”
Get full text

Distributed cooperative spectrum sensing based on consensus optimization by ZENG Fan-zi, LIU Jie, LI Ren-fa, ZENG Qing-guang

“…A distributed wideband spectrum compressive sensing approach based on consensus optimization was developed.The cognitive radio users firstly collected the time-domain sampling by compressive sampling and recover the local spectrum.Then the spectrum of primary users is recovered jointly by cooperative cognitive radio users,which utilizes iterative consensus optimization to reach globally sensing outcomes via one-hop local communication only.In particular,a weighted consensus-averaging constraint is introduced to reduce the number of the consensus constraints,which lowers the computation loads and expedites the convergence.The convergence of the proposed distributed cooperative spectrum sensing scheme is proved analytically.Simulations demonstrate that the proposed scheme can effectively sense the spectrum from compressive sample.…”
Get full text

Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization by Yi Zhang, Weihua Zhang, Jiliu Zhou

“…Sparse-projection image reconstruction is a useful approach to lower the radiation dose; however, the incompleteness of projection data will cause degeneration of imaging quality. As a typical compressive sensing method, total variation has obtained great attention on this problem. …”
Get full text

Efficient 3D imaging method of MIMO radar for moving target by Wei WANG, Ziying HU, Jia’nan YUE

“…When compressive sensing (CS) was used to achieve sparse imaging of moving targets,the Doppler frequency caused by motion will increase the processing dimension,change the center frequency of echo and worsen the mutual coherence property of measurement matrix.In order to improve the three-dimensional (3D) imaging performance of MIMO radar for moving targets,an efficient method was proposed.In each dimension,the distribution information of targets was searched respectively and a new low-dimensional measurement matrix was reconstructed accordingly,so the targets’ area was narrowed down.At the same time,in order to optimize the mutual coherence property of measurement matrix,Bayesian method was used to optimize the velocity-dimensional projection matrix to reduce the strong mutual coherence brought by sampling of Doppler frequency,then the efficient sparse imaging could be achieved.The simulation results show that proposed method can improve the efficiency,accurate imaging performance with efficient.…”
Get full text

Improved Sparse Channel Estimation for Cooperative Communication Systems by Guan Gui, Wei Peng, Ling Wang

“…At first, by using sparse decomposition theory, channel estimation is formulated as a compressive sensing problem. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. …”
Get full text