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Sketched Over-Parametrized Projected Gradient Descent for Sparse Spike Estimation

IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 16-20页

作者： Benard, Pierre-Jean Traonmilin, Yann Aujol, Jean-Francois Univ Bordeaux CNRS Bordeaux INP IMBUMR 5251 F-33400 Talence France

We consider the problem of recovering off-the-grid spikes from linear measurements in the context of Single Molecule Localization Microscopy (SMLM). State of the art model-based methods such as Over-Parametrized Continuous Orthogonal matching pursuit (OP-COMP) with Projected Gradient Descent (PGD) have been shown to successfully recover those signals. The computational cost of these methods scales linearly with the number of measurements. When this number of measurements is large with respect to the dimensionality of the signal, we propose to reduce it with a so-called sketching operator. Based on recent results on compressive sensing in the space of measures, we approximate the ideal sketching operator (benefiting from theoretical recovery guarantees), in the context of SMLM. This sketching method coupled to OP-COMP with PGD shows significant improvements in calculation time in realistic synthetic microscopy experiments.

关键词： Microscopy Position measurement Fourier transforms matching pursuit algorithms Time measurement Superresolution Particle measurements Frequency measurement Context modeling Atmospheric measurements Non-convex optimization spike super-resolution sketching

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Dual-Frequency Distributed Compressive Sensing TomoSAR Method for Reducing Flight Requirements

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Ma, Qian Jiao, Runzhi Li, Jie Shen, Peng Wang, Qingsong Lai, Tao Huang, Haifeng Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518017 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

The existing single-frequency tomographic synthetic aperture radar (TomoSAR) 3-D reconstruction method suffers from insufficient observation information due to the high cost and long period of acquisition of the required multi pass data. To effectively alleviate these problems, it is a feasible solution that a sensor carries dual-frequency radars that can work simultaneously and acquire dual-frequency data for 3-D imaging. This letter systematically introduces the theory and model of dual-frequency TomoSAR 3-D imaging system, and proposes a dual-frequency distributed compressive sensing (DF-DCS) TomoSAR method for reducing flight requirements. First, this method combines the neighborhood pixel information to effectively improve the signal-to-noise ratio (SNR). Then, the reflectivity profiles of the dual-frequency data are jointed to increase the unambiguous height and the accuracy of the target position estimation. Finally, the point-target simulation experiments and surface-target experiments based on SAR simulation (SARSIM) system verify the effectiveness of the proposed method. With the reconstruction accuracy maintained, the DF-DCS method can effectively improve the ability to resolve ambiguity and reduce the required flights.

关键词： Three-dimensional displays Vectors Radar imaging Tomography Radar Spaceborne radar matching pursuit algorithms Accuracy Signal to noise ratio Reflectivity 3-D reconstruction distributed compressive sensing (DCS) dual frequency neighborhood information tomographic synthetic aperture radar (TomoSAR)

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Robust Activity Detection for Massive Access using Covariance-based matching pursuit

Robust Activity Detection for Massive Access using Covarianc...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinjue Wang Esa Ollila Sergiy A. Vorobyov Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We propose a robust activity detection for grant free random access using greedy covariance-learning-based matching pursuit (RCL-MP) algorithm. The method incorporates a robust loss function into the Gaussian negative log-likelihood function, and uses matching pursuit framework for greedily selecting the indices of active users. This algorithm employs a flexible loss function effectively recovering sparse support under non-Gaussian noise conditions. Furthermore, we numerically demonstrate the robustness of RCL-MP across various conditions in massive access scenarios.

关键词： Performance evaluation Accuracy Noise matching pursuit algorithms Signal processing algorithms Robustness Acoustics Speech processing

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Stratigraphic Sequence Correlation of Well Logs Using KANFormer Enhanced With OMP-Based Data Augmentation

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhang, Hao Liu, Naihao Wu, Hao Huo, Jinlong China Univ Geosci Sch Earth Resources Wuhan 430074 Hubei Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China

The stratigraphic sequence correlation of well logs is a crucial step for reservoir investigation. The manual interpretation is a commonly used method, and the experience of geophysicists influences it. Recently, deep learning (DL) has been introduced for automated stratigraphic correlation of well logs. However, building a complete training dataset with high-quality labels is challenging and time-consuming. In this study, we propose an automated stratigraphic sequence correlation method called OMP-KANFormer, which integrates the Kolmogorov-Arnold network with the transformer (KANFormer) model with orthogonal matching pursuit (OMP)-based data augmentation. To solve the problem of training dataset generation, we first propose a data augmentation algorithm based on OMP to simulate waveform features and generate a large number of synthetic well logs as training data. We suggest combining the KANFormer to accurately segment the stratigraphic. The KAN effectively models complex nonlinear relationships, while the transformer excels at capturing long-range dependencies in the features of well logs. Finally, we apply the proposed data augmentation method and KANFormer to a well-log dataset, demonstrating their validity and effectiveness via a comprehensive ablation study and comparisons with widely used SegNet and U-Net.

关键词： Data augmentation Correlation Atoms Geology Transformers Training matching pursuit algorithms Interpolation Dictionaries Oil insulation Automatic stratigraphic correlation Kolmogorov-Arnold network (KAN) orthogonal matching pursuit (OMP)-based data augmentation waveform reconstruction

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Over-the-Air FEEL With Integrated Sensing: Joint Scheduling and Beamforming Design

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第4期24卷 3273-3288页

作者： Asaad, Saba Wang, Ping Tabassum, Hina York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

Employing wireless systems with dual sensing and communications functionalities is becoming critical in next generation of wireless networks. In this paper, we propose a robust design for over-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We first derive novel expressions for the Cramer-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure radar sensing and model aggregation quality, respectively. Then, we develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while keeping the sensing and communication quality, determined respectively in terms of Cramer-Rao bound and achievable downlink rate, in a desired range. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm used widely for sparse recovery in the literature of compressed sensing. The proposed algorithm uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality of the system. It further invokes alternating optimization scheme to iteratively update the downlink beamforming and uplink post-processing by marginally optimizing them in each iteration. Convergence and complexity analysis of the proposed algorithm is presented. Numerical evaluations on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our proposed algorithm. The results show that by leveraging accurate sensing, the target echoes on the uplink signal can be effectively suppressed, ensuring the quality of model aggregation to remain intact despite the interference.

关键词： Sensors Interference matching pursuit algorithms Downlink Array signal processing Uplink Servers Wireless sensor networks Reflection Computational modeling Over-the-air federated learning integrated sensing and computation Cram & eacute r-Rao bound device scheduling matching pursuit algorithm

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Non-Uniform Pilot Pattern Design for Sparse Channel Estimation in MIMO-OFDM Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 3039-3051页

作者： Zhou, Yanxi Gu, Zhi Wang, Shukai Yang, Yang Fan, Pingzhi Southwest Jiaotong Univ CSNMT Int Coop Res Ctr MoST Informat Coding &Transmiss Key Lab Sichuan Prov Chengdu 611756 Peoples R China Southwest Jiaotong Univ Sch Math Chengdu 611756 Peoples R China

In this paper, we propose a non-uniform pilot pattern based on the cyclic difference set (CDS) for sparse channel estimation in multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. The CDS-based pilot patterns can effectively enhance the performance of sparse channel estimation by applying compressed sensing (CS) techniques. However, the existing CDS pilot patterns are limited to single-input-single-output orthogonal frequency-division multiplexing (SISO-OFDM) systems. In MIMO-OFDM systems, as the required number of CDS increases, there will be a growing number of intersecting (overlapping) elements among different CDSs (antennas), thus introducing significant interference among different antennas. To tackle this problem, a special CDS property called identical multi-set intersection (IMI) property is disclosed which can significantly reduce the inter-antenna interference. In addition, two constructions of desired CDS collection D-I having such IMI property are presented. To further eliminate the residual interference due to the remaining intersecting elements, i.e. the exact lambda overlapping positions among all antennas, two pilot placement algorithms, i.e., zero setting (ZS) and partial orthogonal cover coding (POCC), are proposed to achieve orthogonal pilot patterns in frequency. Our simulation results show that our proposed MIMO non-uniform IMI CDS pilot patterns exhibit superior estimation accuracy, closely approaching that of SISO systems. Furthermore, our MSE simulation results also show that, under the same condition, the proposed IMI CDS pilot scheme significantly outperforms the 3GPP 5G NR MIMO-OFDM pilot schemes, offering better estimation performance while maintaining much lower generating complexity compared with other existing multi-antenna pilot schemes.

关键词： OFDM Channel estimation Frequency division multiplexing matching pursuit algorithms Symbols Interference Compressed sensing 5G mobile communication Optimization Estimation compressed sensing (CS) multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) pilot design cyclic difference set (CDS) identical multi-set intersection (IMI)

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Wideband Near-Field Channel Covariance Estimation for XL-MIMO Systems in the Face of Beam Split

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 2912-2926页

作者： Ruan, Chengyao Zhang, Zaichen Jiang, Hao Dang, Jian Wu, Liang Zhang, Hongming Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Lab Nanjing 211111 Peoples R China Nanjing Univ Informat Sci & Technol Coll Artificial Intelligence Nanjing 210044 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Beijing Univ Posts & Telecommun Coll Informat & Commun Engn Beijing 100876 Peoples R China

Extremely large-scale multiple-input-multiple-output (XL-MIMO) techniques are capable of boosting the throughput of next 6G networks. However, due to the large number of antennas in the wideband XL-MIMO systems, they suffer from the so-called near-field beam split effect, where beams at different frequencies are focused on different locations. This effect imposes a severe performance loss on near-field channel estimation. To address this issue, in this paper, we investigate the wideband near-field channel covariance estimation problem of XL-MIMO systems. Explicitly, we first propose a sophisticated polar-domain dictionary for the wideband near-field channel at different frequencies. Specifically, the proposed polar-domain dictionary consists of several sub-matrices. For each sub-matrix, the angle is sampled from a unique range while the distance is sampled from the same range. Meanwhile, considering the beam split effect, we construct the corresponding dictionaries for different frequencies that can beneficially adjust the grid of the sampled angles and distances. Then we propose an algorithm termed as the correlation coefficient sparsity adaptive matching pursuit relying on parameter refinement (CCSAMP-PR) to reconstruct the near-field channel without any prior knowledge of the channel sparsity. More explicitly, an iterative parameter refinement step is conceived for gradually refining the angles and distances per iteration. Furthermore, the proposed algorithm is capable of adaptively adjusting its step size for ensuring rapid convergence. Finally, numerical results are provided for demonstrating that both our proposed polar-domain dictionary and CCSAMP-PR algorithm outperform the family of existing schemes.

关键词： Estimation Wideband matching pursuit algorithms Covariance matrices Dictionaries Vectors Radio frequency Optimization methods OFDM Beam split channel estimation covariance estimation near-field XL-MIMO XL-MIMO

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Joint Signature Estimation and Angular Domain Channel Reuse Based Random Access for Massive Connectivity

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 611-625页

作者： Tang, Xiaobo Li, Li Wang, Qianli Zhang, Xiaoxu Hao, Li Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China

Recently, massive multiple-input multiple-output (mMIMO) aided random access (RA) aroused much research interest in the area of massive machine-type communications (mMTC). Nevertheless, the performance of RA is still constrained by the limited availability of orthogonal preambles and channel resources. To overcome this limitation, we propose to consider the angle-of-arrival (AOA) and time-of-arrival (TOA) information of the transmitted preambles as additional signatures of an active user. To facilitate this signature assignment, a joint signature estimation (JSE) method with effectively suppressed complexity is proposed. Additionally, to enhance the availability of channel resources, we design an angular domain channel reuse (ADCR) scheme that leverages the estimated AOA signature to improve uplink channel efficiency. By applying the JSE and ADCR to a grant-based RA (GBRA) scenario, we demonstrate that our proposed scheme effectively addresses the preamble limitation problem and contributes new channel reuse to the spatial domain, thereby improving RA performance.

关键词： Channel estimation Estimation MIMO communication Meters Vectors Uplink matching pursuit algorithms Massive connectivity random access grant-based RA joint signature estimation channel reuse

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A Low-Complexity Detection Framework for Signed Quadrature Spatial Modulation Based on Approximated MMSE Sparse Detectors

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IEEE SYSTEMS JOURNAL 2025年第1期19卷 32-42页

作者： Albreem, Mahmoud A. Al Habbash, Alaa H. Abu-Hudrouss, Ammar M. EL Astal, M. -t. Univ Sharjah Dept Elect Engn Sharjah 27272 U Arab Emirates Palestinian ICT Res Agcy P ICTRA Gaza 860 Palestine Islamic Univ Gaza Sch Engn Gaza 41256118 Palestine

The design of low-complexity data detection techniques for massive multiple-input multiple-output (mMIMO) systems continues to attract considerable industry and research attention due to the critical need to achieve the right tradeoff between complexity and performance, especially with the signed quadrature spatial modulation (SQSM) scheme. However, the SQSM scheme attains a high spectral efficiency and good performance but suffers from a high computational complexity with mMIMO systems. In this article, we propose an efficient low-complexity detection framework for the SQSM scheme. Sparsity detection is amalgamated in this article with minimum mean-square error (MMSE) detector by decoupling the detection of the real and imaginary vector streams. Unfortunately, the MMSE-based detector has a matrix inversion which incurs a high computational complexity. Therefore, we employed several iterative methods;i.e., conjugate gradient and Gauss-Seidel, to avoid the exact matrix inversion, and hence, the computational complexity is significantly reduced. Moreover, the proposed framework can host other iterative methods such as the JA, successive over relaxation, accelerated over relaxation, Neumann series, Newton iteration, two-parameter over relaxation, and Richardson methods. The proposed detection framework attains a significant complexity reduction with a small or insignificant deterioration in the performance.

关键词： Detectors Symbols Computational complexity Transmitting antennas Iterative methods Receiving antennas Vectors Approximation algorithms matching pursuit algorithms GSM Fifth generation (5G) conjugate gradient (CG) Gauss-Seidel minimum mean-square error (MMSE) massive multiple-input multiple-output (MIMO) (mMIMO) signed quadrature spatial modulation (SQSM)

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A Novel Dispersion Compensation of Lamb Waves by Nonlinear Group Delay Estimation for Defect Imaging

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Li, Shuaiyong Yang, Zhang Zeng, Jianxin Zhang, Chao Chongqing Univ Postsand Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China

The dispersive properties of Lamb waves result in lower accuracy in defect imaging. Some dispersion compensation methods are proposed to enhance imaging accuracy, which rely on known dispersive curves commonly unavailable in practice. In this article, a dispersion compensation method for Lamb waves is introduced to enhance the accuracy of defect imaging. This method utilizes nonlinear group delay estimation (NGDE) and operates when known dispersion curves are unavailable. By replacing the group velocity curve with time-frequency ridges, the problem of unknown dispersion curves is addressed. The traditional Carmona method for ridge extraction is optimized using NGDE to obtain more accurate ridges. Additionally, a method is proposed to calculate compensatory phases based on the group delay (GD) at the central frequency, resulting in nondispersive single-component signals. The ridge extraction and dispersion compensation simulation results demonstrate that the proposed method outperforms the matching pursuit (MP) and dispersion and multimode orthogonal MP (DMOMP) regarding signal-to-noise ratio (SNR) and relative error (RE). Subsequently, the method is also verified by application to defect imaging of delay-and-sum (DAS), weighted DAS (WDAS), and minimum variance distortionless response (MVDR), respectively, which can effectively improve imaging performance.

关键词： Time-frequency analysis Accuracy Simulation Nonlinear distortion Imaging matching pursuit algorithms Delay estimation Data mining Dispersion Signal to noise ratio Defect imaging dispersion compensation group delay (GD) Lamb waves time-frequency ridges

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