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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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Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks

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IEEE WIRELESS COMMUNICATIONS 2024年第5期31卷 42-50页

作者： Xu, Bokai Zhang, Jiayi Du, Hongyang Wang, Zhe Liu, Yuanwei Niyato, Dusit Ai, Bo Letaief, Khaled B. Beijing Jiaotong Univ Beijing Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Nanyang Technol Univ Interdisciplinary Grad Program Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Queen Mary Univ London London England Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Extremely large-scale multiple-input-multiple-out-put (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless systems. The larger array aperture of XL-MIMO makes communication scenarios closer to the near-field region. Therefore, near-field resource allocation is essential in realizing the above key performance indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily depends on the channel characteristics of the selected users, eliminating interference between users through beamforming, power control, and so on. The above resource allocation issue constitutes a complex joint multi-objective optimization problem since many variables and parameters must be optimized, including the spatial degree of freedom, rate, power allocation, and transmission technique. In this article, we review the basic properties of near-field communications and focus on the corresponding "resource allocation" problems. First, we identify available resources in near-field communication systems and highlight their distinctions from far-field communications. Then, we summarize optimization tools, such as numerical techniques and machine learning methods, for addressing near-field resource allocation, emphasizing their strengths and limitations. Finally, several important research directions of near-field communications are pointed out for further investigation.

关键词： Resource management Optimization Antenna arrays signal processing algorithms Communication systems Channel estimation Array signal processing

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Centroid-Free K-Means With Balanced Clustering

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IEEE signal processing LETTERS 2025年 32卷 1191-1195页

作者： Meng, Bin Li, Fangfang Yang, Fan Gao, Quanxue Jilin Police Coll Dept Informat Secur Technol Changchun 130117 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Changchun Humanities & Sci Coll Dept Chinese Language & Literature Changchun 130117 Peoples R China

Currently, a wide array of clustering algorithms have emerged, yet many approaches rely on K-means to detect clusters. However, K-means is highly sensitive to the selection of the initial cluster centers, which poses a significant obstacle to achieving optimal clustering results. Moreover, its capability to handle nonlinearly separable data is less than satisfactory. To overcome the limitations of traditional K-means, we draw inspiration from manifold learning to reformulate the K-means algorithm into a new clustering method based on manifold structures. This method not only eliminates the need to calculate centroids in traditional approaches, but also preserves the consistency between manifold structures and clustering labels. Furthermore, we introduce the l(2,1)-norm to naturally maintain class balance during the clustering process. Additionally, we develop a versatile K-means variant framework that can accommodate various types of distance functions, thereby facilitating the efficient processing of nonlinearly separable data. The experimental results of several databases confirm the superiority of our proposed model.

关键词： Clustering algorithms Manifolds Manifold learning signal processing algorithms Optimization Linear matrix inequalities Kernel Training Geometry Data mining Clustering l(2,1)-norm manifold learning K-means

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2-D Autofocus for High-Squint SAR Based on Affine Coordinate Back-Projection Algorithm

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Lou, Yishan Lin, Hao Chen, Quan Xing, Mengdao Zhou, Shengwei Sun, Guang-Cai Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Shanghai Inst Satellite Engn Shanghai 200240 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

When synthetic aperture radar (SAR) works in high-squint (HS) mode, the interpolation and scaling operation of traditional frequency domain imaging algorithms will change the original structure of motion error, and lead to imaging difficulties. As a classical time-domain imaging algorithm, the back-projection (BP) algorithm is linear processing with a high tolerance for motion error. Therefore, the BP algorithm is very suitable for HS SAR imaging. To further improve the estimation accuracy of motion error, an innovative affine coordinate (AC) system is introduced into the BP algorithm. Based on this AC system, a novel 2-D autofocus algorithm is proposed, which can more accurately estimate and correct the 2-D phase error of the HS SAR BP image. The proposed algorithm has the following advantages: 1) the AC imaging grid is established according to the proposed resolution calculation method based on the BP image spectrum. Under this imaging grid, the nonsystem range cell migration (NsRCM) and the range defocus term of the BP image are significantly reduced, making the phase error estimation more accurate;2) a spectrum alignment processing for BP image in the AC system is proposed to remove the spectrum aliasing so that the azimuth phase error (APE) can be accurately estimated;and 3) the spectrum of the BP image is orthogonal in the AC system, which makes the 2-D phase error compensation based on the established prior phase error structure more accurate. Simulation and real data experiments validate the performance of the proposed algorithm.

关键词： Imaging Synthetic aperture radar Accuracy signal processing algorithms Radar polarimetry Interpolation Radar imaging Frequency-domain analysis Time-domain analysis Image resolution 2-D autofocus affine coordinate (AC) system back-projection (BP) algorithm high-squint (HS) synthetic aperture radar (SAR)

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Joint Pulsed and Continuous Wave Interference Suppression for Dual-Antenna GNSS Receivers

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第2期60卷 2319-2330页

作者： Wang, Wenyi Chen, Haojie Lu, Xuyang Civil Aviat Univ China Tianjin Key Lab Adv Signal Proc Tianjin 300300 Peoples R China

Array antenna is an effective way to improve the interference suppression capability of global navigation satellite System (GNSS) receivers. However, more antennas mean higher costs, which may lead to unacceptable costs in the civil fields. Dual-antenna provides a better compromise between cost and interference suppression capability for those applications in the civil fields. This article proposes a joint-pulsed and continuous wave (CW) interference suppression algorithm for dual-antenna GNSS receivers. To avoid the lack of freedom of the dual antenna, pulsed and CW interference will be suppressed separately. First, in order to avoid the impact of pulses on CW interference suppression capability, a robust covariance matrix estimation method and space-time adaptive processing power inversion algorithm are combined to suppress CW interference. In this stage, the pulses will be deliberately preserved as much as possible. Second, the residual pulses will be suppressed by utilizing a robust pulsed interference suppression method. Simulations and experiments demonstrate that the proposed algorithm can simultaneously suppress joint pulsed and CW interference for a dual-antenna GNSS receiver.

关键词： Interference Interference suppression Estimation Covariance matrices signal processing algorithms Global navigation satellite system Satellites

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Fast Array Ground Penetrating Radar Localization by CNN-Based Optimization Method

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 4663-4673页

作者： Zhou, Changyu Bai, Xu Yi, Li Shah, Munawar Sato, Motoyuki Tong, Xiaohua Tongji Univ Coll Surveying & Geoinformat Shanghai 200070 Peoples R China Harbin Ind Univ Sch Elect & Informat Engn Harbin Peoples R China Osaka Univ Grad Sch Engn Sci Osaka 5650871 Japan Inst Space Technol Dept GNSS Islamabad 44000 Pakistan Tohoku Univ Ctr Northeast Asian Studies Sendai 9808076 Japan Tongji Univ Dept Surveying & Geoinformat Engn Shanghai 200092 Peoples R China

This article presents an optimization-based approach to overcome redundancy arising from the multivariables enumeration process in multiple signal classification (MUSIC). By incorporating Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization, the computational speed of the MUSIC algorithm is significantly improved while maintaining mathematical accuracy. The optimization techniques require reasonable initial values to start the iteration, while for single target imaging purposes, the initial values can be acquired by the boundary between the near field and the far field. To generate suitable initial values for the optimization, we employ a modified convolutional neural network (CNN) to approximate the boundaries between the near and far fields, which vary with array system properties. Besides, the proposed method introduces a method for the Hessian matrix and gradient initialization for the BFGS method. Using simulation results as training samples, the modified CNN successfully establishes boundary approximations. Simulation and experimentation confirm the feasibility of our proposed method, showing its advantages in both accuracy and computation speed compared to existing techniques.

关键词： Multiple signal classification Optimization Training Convolutional neural networks Convolution Surface treatment signal processing algorithms Broyden-Fletcher-Goldfarb-Shanno (BGFS) convolutional neural network (CNN) multiple signal classification (MUSIC) quasi-Newton method

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Nonuniform Sampling Pattern Design for Compressed Spectrum Sensing in Mobile Cognitive Radio Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第9期23卷 8680-8693页

作者： Song, Zihang She, Yiyuan Yang, Jian Peng, Jinbo Gao, Yue Tafazolli, Rahim Kings Coll London Dept Engn London WC2R 2LS England Florida State Univ Dept Stat Tallahassee FL 32306 USA China Acad Informat & Commun Technol Beijing 100191 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Univ Surrey Inst Commun Syst Guildford GU2 7XH England

Compressed spectrum sensing (CSS) plays a pivotal role in dynamic spectrum access within mobile cognitive radio networks by offering reduced power consumption and lower hardware costs. The multicoset sampler, a well-known implementation for periodic nonuniform sampling, has been widely studied and is considered a promising architecture for realizing CSS. This article focuses on the design of the multicoset sampling pattern, aiming at enhancing the isometry property of the sensing matrix. Unlike previous studies which assume a noise-free setup, our work considers the problem in a real-world environment with noise. First, we propose a deterministic algorithm for sampling pattern generation, particularly for specific hardware setup parameters. This algorithm offers strict mutual-coherence control in the multicoset sensing matrix. To address more general hardware configurations, we propose two optimization algorithms. One of them searches for nearly optimal sampling patterns through a random search strategy, while the other employs a greedy pursuit strategy to find a local optimizer. Furthermore, we propose an algorithm to iteratively optimize the sampling pattern between consecutive spectrum sensing windows by minimizing a restricted version of mutual coherence. The excellent performance of our proposed algorithms has been demonstrated through numerical experiments and has been verified on a self-developed hardware platform.

关键词： Sensors Hardware signal processing algorithms Coherence Optimization Matching pursuit algorithms Heuristic algorithms Compressed spectrum sensing dynamic spectrum access sensing matrix design noise-robust algorithms

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Transient Voltage Unit Protection Based on Fault Information Fusion

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IEEE ACCESS 2024年 12卷 184583-184591页

作者： Guo, Zhenwei Zhao, Ruiqiang Li, Haojie Jiang, Yongyan Guilin Univ Elect Sci & Technol Sch Elect & Mech Engn Guilin 541004 Peoples R China

Transient protection has the advantage of ultra-fast operation. However, traditional transient protection is susceptible to the influences of transitional resistance and the initial fault angle, resulting in issues with sensitivity and reliability under weak fault conditions. To address these issues, a new method of transient voltage protection is proposed by mining the equipment configuration information of the bus and line, as well as the frequency characteristics of the line boundary. To solve the problem that single-terminal transient line protection cannot distinguish between line fault and bus fault and the fault of adjacent line head end, a new fault feature is constructed by introducing a difference of instantaneous amplitude of the high-frequency signals between line voltage and bus voltage. To solve the problem that transient protection is susceptible to the influence of fault condition attributes, the fusion of fault characteristics with fault condition attributes results in synthetic fault characteristics, which contain fault attribute information. This approach overcame the adverse effects of fault attributes on transient protection and enhances protection reliability. Transient voltage high-frequency instantaneous amplitudes are obtained and integrated using the Variational Mode Decomposition Hilbert Transform (VMD-HT) to increase the stability of high-frequency fault signal detection. This algorithm solved the original problem of 42.2% cross-data, 41.6% false operations, and 8.9% refusal to operate. It protects both the full length of a transmission line and a busbar at the same time, with twice protection zone comparing with traditional protection. It is simpler, more reliable and more economical. Extensive ATP-Draw simulation experiments have validated the accuracy and reliability of the proposed algorithm.

关键词： Protection Voltage Transient analysis signal processing algorithms Resistance Power system reliability Feature extraction Power transmission lines Power system stability Standards Voltage transient protection fault condition attributes fault information fusion synthetic fault characteristics VMD-HT

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Singular Value Decomposition-Based Adaptive Sampling Approximate Message Passing Net for Sparse-View CT Reconstruction

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2024年第1期16卷 1页

作者： Wu, Zhenhua Xu, Jiafei Yang, Lixia Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China China Elect Technol Grp 38th Res Inst Hefei 230031 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

Sparse-viewcomputed tomography (CT) imaging is a promising technique for reducing radiation dose and accelerating data acquisition in medical imaging. However, the challenges of handling a reduced number of projection views persist for both iterative estimation and deep neural reconstruction methods. In this paper, to address these challenges, we present a singular value decomposition-based adaptive sampling approximate message passing network (ASAMP-Net) sparse-view CT imaging method. To achieve multiple sparse views projection within a single scene imaging and alleviate the computational burden, our proposed ASAMP-Net method incorporates an adaptive sampling module into the AMP deep unrolling network. This module dynamically adjusts the data samples used during the learning process, making our method highly adaptable to various projection matrices. Moreover, by decomposing the projection matrix into its principal components, our approach identifies the respective contributions of independent structures. We then select the most significant principal components to construct a projection matrix model with increased orthogonality, thereby enhancing reconstruction performance. Extensive experiments on public datasets demonstrate the superiority of our method. Notably, ASAMP-Net handles various sparse projection views with just a single training process, achieving prominent imaging results compared to other methods in the literature.

关键词： Image reconstruction Computed tomography Training Matrix decomposition Imaging Iterative methods signal processing algorithms Adaptive sampling ASAMP-Net deep unfolding image reconstruction sparse-view CT

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A Novel Prior-Based Channel Estimation and Activity Detection in Cell-Free mMTC Systems

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5500-5516页

作者： Rajoriya, Anupama Singh, Nakul Budhiraja, Rohit Chaturvedi, Ajit K. IIT Kanpur Dept Elect Engn Kanpur 208016 India Georgia Inst Technol Atlanta GA 30332 USA

We consider the problem of joint channel estimation (CE) and device activity detection (DAD) in the uplink of a cell-free millimeter wave massive multiple-input multiple output (mMIMO) system for massive machine-type communication. We know that the mMIMO channel is spatially correlated, and is sparse in the angular domain. The correlation and sparsity are captured by tailoring a Gaussian prior. This prior is then used to design a centralized variational Bayesian learning (cVBL) algorithm for CE and DAD. The variational approximation in cVBL algorithm reduces its complexity from cubic to linear in terms of devices. We next propose an asynchronous decentralized VBL (adVBL) algorithm, wherein each AP locally estimates its channel from all the devices. The adVBL algorithm is robust to the AP failures, and its complexity is invariant of the number of APs. The adVBL algorithm is developed by reformulating cVBL updates as global optimization problems, and by deriving their local counterparts using the alternating direction method of multipliers. Through extensive numerical studies, we show that the proposed cVBL and adVBL algorithms i) outperform several existing algorithms;ii) require much less pilot overhead;and iii) estimate large-scale fading, unlike the existing ones.

关键词： Approximation algorithms signal processing algorithms Correlation Channel estimation Antennas Millimeter wave communication Complexity theory Optimization Bayes methods Vectors Decentralized algorithm expectation maximization variational Bayesian learning

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