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Physics-Informed Scattering Transform Network for Modulation Recognition in 5G Industrial Cognitive Communications Considering Nonlinear Impairments in Active Phased Arrays

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第1期21卷 425-434页

作者： An, Zeliang Xu, Yuqing Tahir, Abdullah Wang, Jun Ma, Baoze Pedersen, Gert Frolund Shen, Ming Anhui Polytech Univ Sch Comp & Informat Wuhu 241000 Peoples R China Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark Univ Engn & Technol Dept Mechatron Engn Faisalabad Campus Lahore 38000 Pakistan Chongqing Univ Posts & Telecommun Sch Optoelect Engn Chongqing 400065 Peoples R China

Modulation recognition (MR) plays a pivotal role due to its application in the spectrum sensing of 5G industrial cognitive communications and radio interference detection at the physical layer of the Internet of Things (IoT). Previous works have mainly focused on simulated fourth-generation (4G) multicarrier systems and ideal radio frequency (RF) scenarios. To bridge the gap between practice and theory, we propose a viable MR algorithm on all-physical testbeds, with nonlinear impairments of 28 GHz active phased arrays (APA). Specifically, our testbed is built on the Rohde&Schwarz (R&S) vector signal generation R&S-SMBV100B and spectrum analyzer R&S-FSW 67 GHz. To extract salient modulation patterns, we develop a physical-informed scattering transform (SCT) MR network (SCTMR-Net). With SCT modules, SCTMR-Net produces the translation-invariant and deformation-stable representations of 5-G signals by wavelet convolution, nonlinear modulus and low-pass filters. Extensive experiments on real-world measurement verify the viability of SCTMR-Net for high robustness to APA impairments.

关键词： OFDM Convolution Radio frequency Internet of Things Feature extraction Wavelet transforms signal processing algorithms 5-G new radio (NR) industrial cognitive communications deep learning (DL) modulation recognition nonlinear active phased arrays (APA) impairments scattering transform modulation recognition-network (SCTMR-Net)

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A Novel Image Preprocessing Algorithm for Doppler Wind Spectrum Inversion: Principle, Method, and Performance

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhao, Mingguang Yu, Zhibin Li, Mengpei He, Chunhui Wang, Shihai Harbin Inst Technol Shenzhen Sch Aerosp Sci Shenzhen 518071 Peoples R China State Key Lab Solar Act & Space Weather Beijing 100190 Peoples R China

We propose a novel image preprocessing algorithm (IPA) for coherent Doppler wind lidar (CDWL) wind spectrum inversion under low signal-to-noise ratio (SNR) conditions. By utilizing prior knowledge such as the spatial continuity of wind spectra, this new algorithm significantly improves the reliability of the peak frequency estimation and enhances the wind inversion accuracy and precision. The theoretical analysis shows that the IPA can improve the accuracy and precision of the wind spectrum inversion under low SNR, through preselecting and weighting the Doppler wind spectra. Verified by simulation and real CDWL measurements, the IPA effectively enhances the accuracy and reliability of lidar wind spectrum inversion under low SNR conditions. Specifically, the performance enhancement is equivalent to an approximate improvement in the SNR of the data by 2.5 and 5 dB. The measurements with real CDWL measurements show the maximum effective detection range can be increased by more than 2 km. The IPA is low complexity, demanding low computation resources, and can be applied to high temporal resolution Doppler wind estimation.

关键词： Laser radar Doppler effect signal to noise ratio Noise Accuracy signal processing algorithms Estimation Logic gates Optical fiber amplifiers Interference Coherent Doppler wind lidar (CDWL) doppler wind spectrum estimation image preprocessing algorithm (IPA) low signal-to-noise ratio (SNR)

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Design and Validation of a Very Low-Power Phasor Measurement Unit for the Distribution System

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 3553-3562页

作者： Lythgoe, Zachary J. Long, Thomas F. Buchholz, Michael J. Livernois, Anthony R. Kanuteh, Kebba Allee, David R. Pal, Anamitra Graham, Ian R. Drummond, Zachary D. Arizona State Univ Dept Elect Comp & Energy Engn Tempe AZ 85287 USA BG Prod Inc Wichita KS 67213 USA Naval Surface Warfare Ctr Philadelphia PA 19112 USA Univ Penn Dept Phys & Astron Philadelphia PA 19104 USA DEVCOM Army Res Lab Adelphi MD 20783 USA

Phasor measurement units (PMUs) provide a high-resolution view of the power system at the locations where they are placed. As such, it is desirable to place them in bulk in low-voltage distribution circuits. However, the power consumption of a PMU/micro-PMU is on the order of Watts (W), requiring an external power supply, which both increases the overall cost as well as limits the locations where they can be placed. This work details the hardware design of a PMU capable of measuring and reporting voltage and current phasors for a single-phase system at an average power consumption of only 30.8 mW-one to two orders of magnitude lower than existing academic and commercial PMUs. This enables the proposed PMU to run for two weeks using an 11-Wh battery or indefinitely if paired with an inexpensive solar panel. A test bench developed in accordance with the 2018 IEC/IEEE 60255-118-1 PMU Standard confirms the accuracy of this PMU, and a Fluke 6135A PMU calibration system evaluates its performance under realistic conditions. Given its low power consumption, the proposed design is expected to accelerate adoption of PMUs in modern distribution grids.

关键词： Phasor measurement units Sensors Voltage measurement Low-pass filters Conductors Power demand Field programmable gate arrays Batteries signal processing algorithms Sensor arrays Digital signal processing distribution system field-programmable gate array (FPGA) phasor measurement unit (PMU) smart grid

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Greedy Selection for Heterogeneous Sensors

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1394-1409页

作者： Majumder, Kaushani Pillai, Sibi Raj B. Mulleti, Satish Indian Inst Technol Dept Elect Engn Mumbai 400076 India

Simultaneous operation of all sensors in a large-scale sensor network is power-consuming and computationally expensive. Hence, it is desirable to select fewer sensors. A greedy algorithm is widely used for sensor selection in homogeneous networks with a theoretical worst-case performance of ( 1-1/e) approximate to 63% of the optimal performance when optimizing submodular metrics. For heterogeneous sensor networks (HSNs) comprising multiple sets of sensors, most of the existing sensor selection methods optimize the performance constrained by a budget on the total value of the selected sensors. However, in many applications, the number of sensors to select from each set is known apriori and solutions are not well-explored. For this problem, we propose a joint greedy heterogeneous sensor selection algorithm. Theoretically, we show that the worst-case performance of the proposed algorithm is bounded to 50$% of the optimum for submodular cost metrics. In the special case of HSNs with two sensor networks, the performance guarantee can be improved to 63% when the number of sensors to select from one set is much smaller than the other. To validate our results experimentally, we propose a submodular metric based on the frame potential measure that considers both the correlation among the sensor measurements and their heterogeneity. We prove theoretical bounds for the mean squared error of the solution when this performance metric is used. We validate our results through simulation experiments considering both linear and non-linear measurement models corrupted by additive noise and quantization errors. Our experiments show that the proposed algorithm results in 4- 10 dB lower error than existing methods.

关键词： Sensors Sensor phenomena and characterization Costs Measurement Vectors signal processing algorithms Noise measurement Intelligent sensors Greedy algorithms Accuracy Sensor selection heterogeneous sensor networks submodular maximization greedy algorithm frame potential

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The Effectiveness of Local Updates for Decentralized Learning Under Data Heterogeneity

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 751-765页

作者： Wu, Tongle Li, Zhize Sun, Ying Penn State Univ Sch Elect Engn & Comp Sci State Coll PA 16801 USA Singapore Management Univ Sch Comp & Informat Syst Singapore 178902 Singapore

We revisit two fundamental decentralized optimiza-tion methods, Decentralized Gradient Tracking (DGT) and De-centralized Gradient Descent (DGD), with multiple local *** consider two settings and demonstrate that incorporat-ing local update steps can reduce communication ***, for mu-strongly convex andL-smooth loss functions,we proved that local DGT achieves communication complexity O(L/mu(K+1) + delta+mu/mu(1-rho) + rho(1-rho)(2)L+delta/mu), where K is the numberof additional local update,rho measures the network connectivityand delta measures the second-order heterogeneity of the locallosses. Our results reveal the tradeoff between communicationand computation and show increasingKcan effectively reducecommunication costs when the data heterogeneity is low andthe network is well-connected. We then consider the over-parameterization regime where the local losses share the sameminimums. We proved that employing local updates in DGD, evenwithout gradient correction, achieves exact linear convergenceunder the Polyak-& Lstrok;ojasiewicz (PL) condition, which can yielda similar effect as DGT in reducing communication *** of the result to linear models is further provided,with improved rate expression. Numerical experiments validateour theoretical results.

关键词： signal processing algorithms Convergence Complexity theory Optimization Stochastic processes Standards Transient analysis Mesh networks Costs Sun Decentralized optimization data heterogeneity communication efficiency local update

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A Low-Complexity Chinese Remainder Theorem Based Multi-Carrier Delay Estimation Approach

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1643-1657页

作者： Zhang, Yuxiao Wang, Shuai Yang, Xuanhe Zhang, Jiahao Pan, Gaofeng An, Jianping Niyato, Dusit Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore

With the rapid development of cluster systems, such as unmanned aerial vehicle (UAV) networks and satellite constellations, multi-node collaboration has emerged as a critical requirement. Accurate time synchronization, a cornerstone of such collaborative systems, heavily relies on high-precision delay estimation. Traditional multi-carrier delay estimation methods face an inherent trade-off between estimation accuracy and unambiguous range. While the Chinese Remainder Theorem (CRT)-based approach resolves this dilemma by enabling high-precision estimation without sacrificing range, its computational complexity remains prohibitively high for practical implementations. To address this challenge, we propose a novel low-complexity CRT algorithm based on remainder reconstruction (RR-CRT). By introducing an auxiliary phase to reconstruct erroneous remainders, our method reduces the computational complexity from O(K-2) to O(K), where K denotes the number of subcarriers. Crucially, this reduction in complexity only marginally impacts the algorithm's performance, including the phase error tolerance range, the probability of correctly solving phase ambiguity, and the root mean square error (RMSE) of delay estimation. Numerical simulations validate the effectiveness and robustness of the proposed algorithm, demonstrating its superiority in balancing computational efficiency and estimation performance.

关键词： Estimation Delay estimation Symbols Accuracy Computational complexity signal processing algorithms Frequency estimation Synchronization Frequency modulation Bandwidth Chinese remainder theorem multi-carrier low-complexity

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A Robust VMD-Based Modified Multivariable Output Error Statespace Method for Detection of Low-Frequency Oscillations From Noisy signals of WAMS

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IEEE ACCESS 2025年 13卷 2152-2172页

作者： Shetty, Dinesh Prabhu, Nagesh NITTE NMAM Inst Technol Dept Elect & Elect Engn Nitte 574110 Karnataka India

A poorly damped set of power oscillations interconnected power systems may lead to system-wide breakups or considerably reduce the power transfer over critical channels and corridors. As a result, identifying and suppressing these oscillatory modes is a critical challenge in modern power systems. Advances in wide area monitoring systems in modern power system facilitates the numerous data for the analysis of the system characteristics. Wide Area Monitoring Systems (WAMS) have enabled the collection of large amounts of data, aiding in system analysis. Phasor measurement units may experience noisy signals during measurement which may lead to poor identification of low frequency oscillatory modes or introduce extraneous modes during analysis using state of the art methods. This research examines the performance of the Robust Variable Mode Decomposition (VMD) technique for analyzing signals contaminated with noise. The study employs particle swarm optimization to determine the appropriate quantity of Intrinsic Mode Functions (IMFs). Additionally, it applies Anderson-Darling (AD) and Kolmogorov-Smirnov (KD) statistical distance measures to identify the effective number of IMFs, enabling the differentiation between signal IMFs and noise IMFs. Wavelet de-noising technique is employed for the de-noising the sum of chosen IMF signals. To validate the selection of adequate IMFs a heuristic approach is used to monitor the signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for effectively seperating noisy contents from actual content of the signal. The proposed system incorporates the wavelet based filter in conventional VMD which not only helps in fetching the frequency but also helps in filtering the noise with appropriate selection of the mother wavelet, i.e. it utilizes the fusion of VMD and Wavelet Transform. The approach leverages the adaptability of VMD's decomposition alongside the precise denoising capabilities of WT, resulting in high effectiveness for signals th

关键词： Oscillators Power system stability signal processing algorithms Noise measurement Transforms Noise Wavelet transforms Damping Noise reduction Eigenvalues and eigenfunctions Anderson Darlington intrinsic mode functions low frequency oscillations variable mode decomposition

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Memory-Enhanced Distributed Accelerated algorithms for Coordinated Linear Computation

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2025年 11卷 35-47页

作者： Ding, Shufen Meng, Deyuan Du, Mingjun Cai, Kaiquan Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA State Key Lab CNS ATM Beijing 100191 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Informat & Automat Engn Jinan 250353 Peoples R China Beihang Univ BUAA Sch Elect & Informat Engn Beijing 100191 Peoples R China

In this paper, a memory-enhanced distributed accelerated algorithm is proposed for solving large-scale systems of linear equations within the context of multi-agent systems. By employing a local predictor consisting of a linear combination of the nodes' current and previous values, the inclusion of two memory taps can be characterized such that the convergence of the distributed solution algorithm for coordinated computation is accelerated. Moreover, consensus-based convergence results are established by leveraging an analysis of the spectral radius of an augmented iterative matrix associated with the error system that arises from solving the equation. In addition, the connection between the convergence rate and the tunable parameters is developed through an examination of the spectral radius of the iterative matrix, and the optimal mixing parameter is systematically derived to achieve the fastest convergence rate. It is shown that despite whether the linear equation of interest possesses a unique solution or multiple solutions, the proposed distributed algorithm exhibits exponential convergence to the solution, without dependence on the initial conditions. In particular, both the theoretical analysis and simulation examples demonstrate that the proposed distributed algorithm can achieve a faster convergence rate than conventional distributed algorithms for the coordinated linear computation, provided that adjustable parameters are appropriately selected.

关键词： Convergence Distributed algorithms signal processing algorithms Prediction algorithms Vectors Jacobian matrices Symmetric matrices Multi-agent systems Information processing Distributed databases Convergence rate consensus distributed algorithms iterative method multi-agent systems

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Distributed Adaptive Spatial Filtering With Inexact Local Solvers

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1262-1277页

作者： Hovine, Charles Bertrand, Alexander STADIUS Ctr Dynam Syst Signal Proc & Data Analyt B-3001 Leuven Belgium Katholieke Univ Leuven Leuven AI Inst Artificial Intelligence B-3001 Leuven Belgium

The Distributed Adaptive signal Fusion (DASF) framework is a meta-algorithm for computing data-driven spatial filters in a distributed sensing platform with limited bandwidth and computational resources, such as a wireless sensor network. The convergence and optimality of the DASF algorithm has been extensively studied under the assumption that an exact, but possibly impractical solver for the local optimization problem at each updating node is available. In this work, we provide convergence and optimality results for the DASF framework when used with an inexact, finite-time solver such as (proximal) gradient descent or Newton's method. We provide sufficient conditions that the solver should satisfy in order to guarantee convergence of the resulting algorithm as well as numerical simulations to validate these theoretical results.

关键词： signal processing algorithms Convergence Wireless sensor networks Optimization Distributed databases Principal component analysis Europe Artificial intelligence Adaptive systems Spatial filters Distributed signal processing wireless sensor networks adaptive spatial filtering optimization

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Spectrum Management in Near-Field Multi-User Wireless Communications

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IEEE COMMUNICATIONS MAGAZINE 2025年第5期63卷 222-227页

作者： Ghasempour, Yasaman Madanayake, Arjuna Jornet, Josep M. Princeton Univ Elect & Comp Engn Princeton NJ 08544 USA Florida Int Univ Elect & Comp Engn Miami FL USA Northeastern Univ Elect & Comp Engn Boston MA USA Northeastern Univ Res Coll Engn Boston MA USA Northeastern Univ Inst Wireless Internet Things Boston MA USA Northeastern Univ Ultrabroadband Nanonetworking UN Lab Boston MA USA

Employing massive antenna arrays and reconfigurable intelligent surfaces together with recent exploration of higher spectral bands (i.e., mmWave and sub-terahertz frequencies) has unleashed an unprecedented territory where receivers located up to tens of meters from the base station can be within its near-field. In this regime, the typical assumption of far-field channels and planar wave-fronts can fail in practical wireless network scenarios. Indeed, in almost all prior work on spectrum management and spatial-spectral reuse, the impact of near-field propagation has been ignored. This article discusses the opportunities and challenges in spectrum management specific to near-field wireless communications. First, this article provides a comprehensive view of the interference models and their differences in near vs far fields. Second, this article illustrates the opportunities presented by exploiting new types of beams that are only available in the near field for multi-user interference and spectrum sharing. Notably, the near-field characteristics are viewed as an opportunity to enhance spatial-spectral efficiency rather than a problem to overcome. Finally, this article provides new trends in digital signal processing for wideband near-field communications and open research challenges.

关键词： Wireless networks signal processing algorithms Receiving antennas Interference Reconfigurable intelligent surfaces Sensors Millimeter wave communication Wideband Radio spectrum management Antenna arrays

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