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Power Optimization and Deep Learning for Channel Estimation of Active IRS-Aided IoT

IEEE INTERNET OF THINGS JOURNAL 2024年第24期11卷 41194-41206页

作者： Wang, Yan Dong, Rongen Shu, Feng Gao, Wei Zhang, Qi Liu, Jiajia Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Hainan Univ Collaborat Innovat Ctr Informat Technol Haikou 570228 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian 710072 Shaanxi Peoples R China

In this article, channel estimation (CE) of an active intelligent reflecting surface (IRS) aided uplink Internet of Things (IoT) network is investigated. First, the least square (LS) estimators for the direct channel and the cascaded channel are presented, respectively. The corresponding mean-square errors (MSEs) of channel estimators are derived. Subsequently, in order to evaluate the influence of adjusting the transmit power at the IoT devices or the reflected power at the active IRS on Sum-MSE performance, two situations are considered. In the first case, under the total power sum constraint of the IoT devices and active IRS, the closed-form expression of the optimal power allocation (PA) factor is derived. In the second case, when the transmit power at the IoT devices is fixed, there exists an optimal reflective power at active IRS. To further improve the estimation performance, the convolutional neural network (CNN)-based direct CE (CDCE) algorithm and the CNN-based cascaded CE (CCCE) algorithm are designed. Finally, simulation results demonstrate the existence of an optimal PA strategy that minimizes the Sum-MSE, and further validate the superiority of the proposed CDCE/CCCE algorithms over their respective traditional LS and minimum MSE (MMSE) baselines.

关键词： Internet of Things Channel estimation Uplink signal processing algorithms Resource management Estimation Performance evaluation Active intelligent reflecting surface (IRS) channel estimation (CE) deep learning (DL) Internet of Things (IoT) power optimization

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Full-Dimensional Partial-Search Generalized Radon-Fourier Transform for High-Speed Maneuvering Target Detection

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 5445-5457页

作者： Jiang, Wenqi Liu, Hongwei Jiu, Bo Zhao, Yu Li, Kang Zhang, Yu Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

Long time coherent integration (LTCI) for high-speed maneuvering targets in radar signal processing poses substantial difficulties due to the occurrence of range migration (RM) and Doppler frequency migration (DFM). The generalized Radon-Fourier transform (GRFT), an effective method for accumulating target energy, is hampered by its high computational cost. To address this, the full-dimensional partial-search generalized Radon-Fourier transform (FSGRFT), a fast implementation of the GRFT algorithm, is proposed in this article. Differing from the exhaustive full-dimensional search of the GRFT algorithm, the FSGRFT algorithm first employs a pretrained residual network to coarse estimate range cells where targets might exist, along with their corresponding motion parameter subspaces. Guided by this initial coarse estimate, the FSGRFT algorithm narrows its search to a subset of range cells and the designated subspace of motion parameters, thereby considerably decreasing the number of searches. Meanwhile, the introduction of a lightweight network structure with robust representation capabilities ensures the accuracy of the coarse estimation, without bringing excessive computational cost. Therefore, the FSGRFT algorithm obtains a good tradeoff between the computational cost and integration performance, in comparison with other LTCI algorithms. Finally, simulation results demonstrate that the FSGRFT algorithm can achieve detection performance comparable to the GRFT algorithm, but with considerably lower computational cost.

关键词： signal processing algorithms Computational efficiency Radar signal processing Doppler effect Radar Object detection Task analysis Doppler frequency migration (DFM) high-speed maneuvering targets detection range migration (RM) residual network

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Stable Estimation of Pulses of Unknown Shape From Multiple Snapshots via ESPRIT

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2637-2648页

作者： Kalra, Meghna Lee, Kiryung Ohio State Univ Dept Elect & Comp Engn Columbus OH 43220 USA

We consider the problem of resolving overlapping pulses from noisy multi-snapshot measurements, which has been a problem central to various applications including medical imaging and array signal processing. ESPRIT algorithm has been used to estimate the pulse locations. However, existing theoretical analysis is restricted to ideal assumptions on signal and measurement models. We present a novel perturbation analysis that overcomes the previous theoretical limitation, which is derived without a stringent assumption on the signal model. Our unifying analysis applies to various sub-array designs of the ESPRIT algorithm. We demonstrate the usefulness of the perturbation analysis by specifying the result in two practical scenarios. In the first scenario, we quantify how the number of snapshots for stable recovery scales when the number of Fourier measurements per snapshot is sufficiently large. In the second scenario, we propose compressive blind array calibration by ESPRIT with random sub-arrays and provide the corresponding non-asymptotic error bound applying uniformly to all well-separated pulses. Furthermore, we demonstrate that the empirical performance of ESPRIT corroborates the theoretical analysis through extensive numerical results.

关键词： Shape Noise measurement Perturbation methods Estimation Calibration signal processing algorithms Noise ESPRIT blind deconvolution super-resolution array calibration perturbation analysis

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Effective Coherent Integration of Agile Echo signal via Improved Sparse Adaptive Matching Pursuit

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IEEE signal processing LETTERS 2025年 32卷 1420-1424页

作者： Lang, Ping Fu, Xiongjun Dong, Jian Yin, Junjun Yang, Jian Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

Radar transmits active agile waveformplays a significant role in anti-jamming. However, efficient coherent integration of target's agile echo in a coherent processing interval (CPI) usually poses a severe challenge. This letter proposes an improved sparsity adaptive matching pursue (ISAMP) to address this issue. Firstly, the agile echo signal model of random interpulse frequency and PRT joint agile (RI-FPrtJA) waveform is derived;The sparse reconstruction model of RI-FPrtJA echo signal is then mathematically deduced based on compress sensing theory;Lastly, the ISAMP is proposed to accurately accomplish sparse reconstruction based on the regularized grids mismatch correction and adaptive searching step extension. The simulation results demonstrate that the ISAMP method can achieve better coherent integration in terms of mismatch sidelode levels suppression, sparse reconstruction capacity, and computational cost, compared to some current existing methods.

关键词： Doppler effect Dictionaries Radar Frequency modulation Vectors Sparse matrices Sensors signal processing algorithms Mathematical models Adaptation models Radar jamming suppression agile waveform coherent integration sparsity adaptive matching pursue

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Variational Learning algorithms for Channel Estimation in RIS-Assisted mmWave Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第1期72卷 222-238页

作者： Nakul, Milind Rajoriya, Anupama Budhiraja, Rohit Georgia Inst Technol Atlanta GA 30332 USA Indian Inst Technol Kanpur IIT Kanpur Dept Elect Engn Kanpur 208016 India

We consider the problem of estimating channel in reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) systems. We propose two variational expectation maximization (VEM) based channel estimation algorithms, which exploit the angular domain sparsity of RIS-assisted mmWave channel. To fully capture this sparsity, both within and across UEs, we construct a novel column-wise coupled Gaussian prior. The first proposed structured-mean-field-based VEM (SMF-VEM) algorithm uses the proposed prior, and calculates the posterior distribution of the unknown channel by assuming that it belongs to a set of multivariate distributions. This algorithm inverts a high-dimensional matrix in its posterior update, and consequently does not scale well for a large number of RIS elements and base station antennas, which are commonly used in practical systems. The second proposed fast mean field-based VEM (FMF-VEM) algorithm reduces complexity by assuming a fully-factorized posterior. It also bounds the variational objective to remove the residue coupling between the channel and phase matrices. Using extensive numerical investigations for a practical RIS mmWave system, and by using multiple metrics, we show that the proposed i) SMF- and FMF-VEM algorithms outperform several of their state-of-the-art counterparts;and ii) FMF-VEM has a much lower time complexity than SMF-VEM.

关键词： Channel estimation Approximation algorithms Millimeter wave communication Sparse matrices Matching pursuit algorithms Bayes methods signal processing algorithms Variational inference expectation maximization sparse Bayesian learning (SBL)

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Distributed Optimization Methods for Multi-robot Systems: Part 2-A Survey

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IEEE ROBOTICS & AUTOMATION MAGAZINE 2024年第3期31卷 154-169页

作者： Shorinwa, Ola Halsted, Trevor Yu, Javier Schwager, Mac Stanford Univ Dept Mech Engn Stanford CA 94305 USA Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA

Although the field of distributed optimization is well developed, relevant literature focused on the application of distributed optimization to multi-robot problems is limited. This survey constitutes the second part of a two-part series on distributed optimization applied to multi-robot problems. In this article, we survey three main classes of distributed optimization algorithms-distributed first-order (DFO) methods, distributed sequential convex programming methods, and alternating direction method of multipliers (ADMM) methods-focusing on fully distributed methods that do not require coordination or computation by a central computer. We describe the fundamental structure of each category and note important variations around this structure, designed to address its associated drawbacks. Further, we provide practical implications of noteworthy assumptions made by distributed optimization algorithms, noting the classes of robotics problems suitable for these algorithms. Moreover, we identify important open research challenges in distributed optimization, specifically for robotics problem.

关键词： Optimization Surveys Robot kinematics Robot sensing systems signal processing algorithms Heuristic algorithms Approximation algorithms

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ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2024年第7期71卷 2143-2153页

作者： Chen, Long Jiang, Zheheng Barker, Joseph Zhou, Huiyu Schlindwein, Fernando Nicolson, Will Ng, G. Andre Li, Xin UCL Dept Med Phys & Biomed Engn London England Univ Leicester Sch Comp & Math Sci Leicester England Imperial Coll London Fac Med London England Univ Leicester Sch Engn Leicester England Univ Leicester Dept Cardiovasc Sci Leicester England Univ Leicester Sch Engn Leicester LE1 7RH England

Electrocardiogram (ECG) delineation to identify the fiducial points of ECG segments, plays an important role in cardiovascular diagnosis and care. Whilst deep delineation frameworks have been deployed within the literature, several factors still hinder their development: (a) data availability: the capacity of deep learning models to generalise is limited by the amount of available data;(b) morphology variations: ECG complexes vary, even within the same person, which degrades the performance of conventional deep learning models. To address these concerns, we present a large-scale 12-leads ECG dataset, ICDIRS, to train and evaluate a novel deep delineation model-ECGVEDNET. ICDIRS is a large-scale ECG dataset with 156,145 QRS onset annotations and 156,145 T peak annotations. ECGVEDNET is a novel variational encoder-decoder network designed to address morphology variations. In ECGVEDNET, we construct a well-regularized latent space, in which the latent features of ECG follow a regular distribution and present smaller morphology variations than in the raw data space. Finally, a transfer learning framework is proposed to transfer the knowledge learned on ICDIRS to smaller datasets. On ICDIRS, ECGVEDNET achieves accuracy of 86.28%/88.31% within 5/10 ms tolerance for QRS onset and accuracy of 89.94%/91.16% within 5/10 ms tolerance for T peak. On QTDB, the average time errors computed for QRS onset and T peak are -1.86 +/- 8.02 ms and -0.50 +/- 12.96 ms, respectively, achieving state-of-the-art performances on both large and small-scale datasets. We will release the source code and the pre-trained model on ICDIRS once accepted.

关键词： Electrocardiography Morphology Transfer learning signal processing algorithms Deep learning Computational modeling Biomedical engineering ECG delineation fiducial points detection variational encoder-decoder transfer learning

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Development of a Sound Directional Device Based on Ultrasound Modulation and Transducer Array Optimization

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Ma, Zhexuan Zhao, Heng Long, Zhili Ju, Jianzhong Liu, Xicheng Harbin Inst Technol Shenzhen Sch Mech Engn & Automat Shenzhen 518071 Peoples R China

The sound directional system utilizes the directional transmitting of ultrasonic wave to realize the directivity of sound, which has a wide application in civil and military fields. An optimized sound directional device is developed in this article. The signal modulation algorithms with single-frequency and broadband frequency input are theoretically analyzed. The modulation coefficients on sound distortion are evaluated. The sound fields are simulated and show that the sound emitting areas are 0.09 pi and 0.16 pi for the 16- and 10-mm-diameter transducers in 40 kHz, indicating that the 16-mm transducer has better sound directivity. The hardware of sound directional device is established, and the signal modulation is realized by DSP programming. Sound directivity and distortion evaluation principle is proposed, which provides a guidance of ultrasound modulation determination and transducer array optimization. Sound distortion measurement is conducted, and the second-order approximate square root (SASR) is adopted in the proposed hardware. Finally, the sound directional system is compared with the current sound directional array. Experiments show that the total harmonic distortion (THD) is 15.44% of 7x7 array. The sidelobe amplitude intensity of 7x7 array is 71.4 dB and lower than 87.9 dB of the commercial array, demonstrating that the sound emitting of 7x7 array in sidelobe is in high suppression. The sound main beam emitting angle of 7x7 array is 117 degrees, which is significantly lower than 175 degrees of the Audfly array. Therefore, the 7x7 array shows a better sound directivity.

关键词： Distortion Acoustics Amplitude modulation Acoustic distortion signal processing algorithms Frequency modulation Harmonic distortion Harmonic analysis Optimization Array signal processing signal modulation sound directional system sound directivity sound distortion ultrasonic array

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Spatially Variant Filtering Network Based on Generalized Optimal Constraints for Sidelobe Suppression in SAR Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Suo, Yuxi Fu, Kun Wu, Youming Meng, Qingbiao Miao, Tian Diao, Wenhui Sun, Xian Chinese Acad Sci Aerosp Informat Res Inst Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Network Informat Syst Technol NIST Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100190 Peoples R China

Sidelobes commonly disturb synthetic aperture radar (SAR) image understanding and interpretation. Traditional spatially variant filtering algorithms achieve a superior tradeoff between sidelobe suppression and resolution preservation by means of adaptively calculating filtering parameters under some specific restrictions, such as filter design restriction and minimum amplitude constraint (MAC). These restriction aims to obtain an efficient analytical solution for filters, which is easy to calculate under unsupervised conditions. However, the restriction scope is so narrow that the suppression performance achieved by these filters is limited. Also, since the unsupervised optimization based on MAC indiscriminately minimizes amplitude, the main-lobe loss is unavoidable. To further improve the performance, a spatially variant convolution neural network (SVNN) is proposed, which consists of two core modules. One is the spatially variant filter generation (SVFG) module, adaptively generating superior spatially variant filters under more relaxed restrictions. The other is a paralleled shifted convolution (PSC) module, converting the signal format to achieve a fast and parallel spatially variant filtering process. Benefiting from more relaxed filter restrictions, the novel network successfully achieves better performance on sidelobe suppression. In addition, with supervised optimization based on another more accurate restriction, namely, minimum error constraint (MEC), the proposed algorithm also achieves superior main-lobe maintenance. All of them are validated by comparative experiments based on satellite data from GaoFen-3 and TerraSAR-X, and our proposed method achieves state-of-the-art performance. The entire project is available at https://***/suoyuxi/SVNN.

关键词： Optimization signal processing algorithms Radar polarimetry Maintenance Synthetic aperture radar signal resolution Convolutional neural networks Computer aided software engineering Spatial resolution Solids Deep learning sidelobe suppression spatially variant filtering synthetic aperture radar (SAR)

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OSA-PD: Open-Set Active Learning Exploiting Predictive Discrepancy

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IEEE signal processing LETTERS 2025年 32卷 851-855页

作者： Chen, Zhiming Han, Peng Jiang, Fei Si, Jiaxin Xiong, Lili Chongqing Univ Posts & Telecommun Sch Software Engn Chongqing 400065 Peoples R China Chongqing Acad Sci & Technol Chongqing 400065 Peoples R China Chongqing Qulian Digital Technol Co Ltd Chongqing 400065 Peoples R China

Active learning with closed-set annotation has achieved significant success. However, real-world data often comprises numerous unknown classes irrelevant to the task which often confuses the query strategies to select unknown class data for annotation. To address such challenge in the open-set annotation (OSA), we propose a novel open-set active learning framework based on predictive discrepancy, named OSA-PD, with consideration that valuable data for model improvement is often with high predictive discrepancy. Specifically, two algorithms based on different discrepancy measurements are presented under OSA-PD framework, i.e., OSA-PRD with predictive results discrepancy, and OSA-PDD with decoupled predictive distribution discrepancy. Experimental results on CIFAR100 and TinyImageNet demonstrate the proposed OSA-PD can effectively select known class data and achieve higher classification accuracy with the same amount of annotated sample in comparison with existing active learning algorithms.

关键词： Detectors Annotations Active learning Data models Accuracy Probability distribution Prediction algorithms Uncertainty Training signal processing algorithms open-set annotation predictive discrepancy image classification

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