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Globally Optimal Max-Min Rate Joint Channel and Power Allocation for Hybrid NOMA-OMA Downlink Systems

IEEE TRANSACTIONS ON signal processing 2025年 73卷 1674-1690页

作者： Sultana, Tanin Dumitrescu, Sorina McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada

This work proposes a globally optimal solution algorithm to the joint power allocation (PA) and channel allocation (CA) problem for downlink hybrid NOMA-OMA systems with the objective of maximizing the minimum user rate. In the hybrid NOMA-OMA scenario, the users are divided into clusters, each cluster shares one channel using NOMA (Non-Orthogonal Multiple Access), while different clusters are assigned channels orthogonally. The optimization problem is converted to the problem of maximizing the user rate under the constraint that all rates be equal. It is further decomposed into PA and CA subproblems, which are solved iteratively. The PA subproblem is handled by first deriving an analytical expression of the total power as a function of the common user rate, and then solving it via bisection search. The CA subproblem keeps the equal-rate assignment fixed and aims to find the CA that minimizes the total power. We prove that the CA subproblem is equivalent to a minimum bipartite graph matching problem, for which efficient algorithms exist. Finally, we demonstrate that the proposed iterative algorithm converges to the globally optimal solution after a finite number of iterations. In addition, we prove that the number of iterations is at most three when the power budget is sufficiently large. Extensive experiments demonstrate the effectiveness of the proposed scheme.

关键词： NOMA Resource management Bipartite graph Minimax techniques Fading channels Downlink Channel allocation Training signal processing algorithms Linear programming power allocation user clustering channel allocation max-min fairness bipartite graph matching

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Robust Direction-of-Arrival Estimation With Outliers and Partly Calibrated Uniform Linear Array

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4825-4834页

作者： Xiao, Wudang Li, Yingsong Yu, Canping Zhao, Luyu Pan, Mengguan de Lamare, Rodrigo C. Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Pontif Catholic Univ Rio De Janeiro PUC Rio Ctr Telecommun Res CETUC BR-22451900 Gavea Brazil Univ York Dept Elect Engn York YO10 5DD England

This article presents a three-step direction-of-arrival estimation method aimed at addressing impulsive noise and array gain-phase errors. Initially, a robustness function combining the logarithmic function with least squares is introduced to address impulsive noise. This robust function is utilized to formulate a low-rank matrix approximation problem for acquiring noise-free received data, which is solved using the alternating-direction method of multipliers. Subsequently, exploiting the obtained noise-free received data, the array gain-phase errors are estimated using the root-rank reduction estimator. Finally, based on the acquired noise-free received data and gain-phase errors, a minimization problem incorporating the logarithmic function is formulated. To efficiently solve this minimization problem, it is transformed into a weighted l(2,1)-norm minimization problem, and the fast-iterative-shrinkage-thresholding algorithm is employed. Compared to existing methods, the proposed approach demonstrates superior estimation accuracy and precision while achieving an attractive tradeoff between performance and computational complexity.

关键词： Direction-of-arrival estimation Estimation Vectors signal processing algorithms Sensor arrays Multiple signal classification Minimization Manifolds Gaussian noise Fitting Direction-of-arrival (DOA) estimation gain-phase error log-sum minimization outliers partly calibrated array sparse signal recovery

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Deep Unfolding Beamforming and Power Control Designs for Multi-Port Matching Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 1401-1414页

作者： Xu, Bokai Zhang, Jiayi Lin, Qingfeng Xiao, Huahua Wu, Yik-Chung Ai, Bo State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China ZTE Corp State Key Lab Mobile Network & Mobile Multimedia T Shenzhen 518057 Peoples R China

The key technologies of sixth generation (6G), such as ultra-massive multiple-input multiple-output (MIMO), enable intricate interactions between antennas and wireless propagation environments. As a result, it becomes necessary to develop joint models that encompass both antennas and wireless propagation channels. To achieve this, we utilize the multi-port communication theory, which considers impedance matching among the source, transmission medium, and load to facilitate efficient power transfer. Specifically, we first investigate the impact of insertion loss, mutual coupling, and other factors on the performance of multi-port matching networks. Next, to further improve system performance, we explore two important deep unfolding designs for the multi-port matching networks: beamforming and power control, respectively. For the hybrid beamforming, we develop a deep unfolding framework, i.e., projected gradient descent (PGD)-Net based on unfolding projected gradient descent. For the power control, we design a deep unfolding network, graph neural network (GNN) aided alternating optimization (AO)-Net, which considers the interaction between different ports in optimizing power allocation. Numerical results verify the necessity of considering insertion loss in the dynamic metasurface antenna (DMA) performance analysis. Besides, the proposed PGD-Net based hybrid beamforming approaches approximate the conventional model-based algorithm with very low complexity. Moreover, our proposed power control scheme has a fast run time compared to the traditional weighted minimum mean squared error (WMMSE) method.

关键词： Array signal processing Antennas Wireless communication Mutual coupling Resource management Optimization signal processing algorithms Radio frequency Power control Integrated circuit modeling Matching networks circuit theory deep unfolding mutual coupling beamforming

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Improved Phase Generated Carrier Demodulation for Time-Division Multiplexed FBG Accelerometers

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第2期43卷 942-947页

作者： Li, Tingfeng Chen, Fengyi Wang, Ruohui Qiao, Xueguang Univ Shaanxi Prov Engn Res Ctr Opt Fiber Well Logging Technol Oil & Xian 710127 Peoples R China Northwest Univ Sch Phys Xian 710127 Peoples R China

A phase generated carrier (PGC) demodulation system for fiber Bragg grating (FBG) time-domain multiplexed accelerometers array is proposed. To eliminate environmental noise effect on the Michelson interferometer(MI), a reference FBG is used. Additionally, a PGC-improved demodulation algorithm is proposed to facilitate precise demodulation of the acceleration signal. The distortion of the demodulated signal due to modulation depth fluctuations is eliminated by constructing a quadrature signal containing only the test signal. The numerical simulation results show that the output signal of the PGC-Improved demodulation algorithm is independent of the operating point of the interferometer, the carrier delay of the modulation signal, and the modulation depth. The vibration test results of the FBG accelerometer based on the flexible hinge show that the PGC-Improved algorithm achieves a gain of 15.01 dB and 57.2 dB relative to PGC-DCM and PGC-Arctan at frequencies and accelerations of 60 Hz and 1 g. The minimum total harmonic distortion value of the PGC-Improved algorithm is 0.008 at modulation depths from 1.5 rd to 3.5 rd. The noise level of the system is approximately 4 x 10(-5) rad provided that the MI is subjected to 60 Hz perturbation.

关键词： Demodulation Delays Modulation signal processing algorithms Fiber gratings Phase modulation Accelerometers Fiber-optic sensors phase generated carrier reference FBG

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Generalization Error Matters in Decentralized Learning Under Byzantine Attacks

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 843-857页

作者： Ye, Haoxiang Ling, Qing Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

Recently, decentralized learning has emerged as a popular peer-to-peer signal and information processing paradigm that enables model training across geographically distributed agents in a scalable manner, without the presence of any central server. When some of the agents are malicious (also termed as Byzantine), resilient decentralized learning algorithms are able to limit the impact of these Byzantine agents without knowing their number and identities, and have guaranteed optimization errors. However, analysis of the generalization errors, which are critical to implementations of the trained models, is still lacking. In this paper, we provide the first analysis of the generalization errors for a class of popular Byzantine-resilient decentralized stochastic gradient descent (DSGD) algorithms. Our theoretical results reveal that the presence of Byzantine agents introduces additional error terms in the generalization error bounds, which are independent on the number of training samples. Numerical experiments are conducted to confirm our theoretical results.

关键词： Training Stochastic processes Optimization signal processing algorithms Federated learning Distance learning Computer aided instruction Aggregates Uncertainty Topology Decentralized learning Byzantine-resilience generalization error

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FPGA Implementation of Staged Projection Refining Multiple Orthogonal Matching Pursuit Algorithm for Compressed Sensing

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年第5期33卷 1334-1347页

作者： Liu, Sujuan Liang, Yichen Zhang, Zixing Wan, Peiyuan Beijing Univ Technol Coll Microelect Beijing 100124 Peoples R China

Reconstruction algorithms are an integral part of compressed sensing (CS) theory, which can reliably reconstruct the original signal from the low-dimensional compressed signal. The orthogonal matching pursuit (OMP) algorithm has been widely studied and extensively selected in hardware implementations. However, the low reconstruction success rate of the OMP algorithm under high sparsity conditions has led to the proposal and application of more reconstruction algorithms in hardware implementations. In this article, a staged projection refining multiple OMP (SPR-MOMP) algorithm is proposed based on the OMP algorithm. This algorithm improves the reconstruction accuracy by refining the support set using a staged backtracking strategy. It also employs a multiple-atom selection strategy for parallel expansion of the support set, ensuring reconstruction efficiency. The reconstruction simulation demonstrates that the SPR-MOMP algorithm achieves a higher reconstruction success rate than the OMP algorithm, with fewer iterations. A hardware architecture applying the SPR-MOMP algorithm is designed and implemented on a Virtex UltraScale+ field-programmable gate array (FPGA) with N = 1024, M = 256, and K = 36. The proposed architecture achieves a reconstruction signal-to-noise ratio (RSNR) of 44.27 dB, with 20-bit data width and 15-bit fractional width. The maximum clock frequency of the architecture is 200 MHz, enabling reconstruction within 276.6 mu s. The proposed architecture achieves a lower dynamic power consumption of 1929 mW.

关键词： Computer architecture Matching pursuit algorithms Hardware Field programmable gate arrays Image reconstruction Heuristic algorithms Backtracking Reconstruction algorithms signal processing algorithms Sensors Compressed sensing (CS) field-programmable gate array (FPGA) hardware reconstruction orthogonal matching pursuit (OMP) QR decomposition (QRD)

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An Unbalanced Optimal Transport-Based Approach for Robust Dictionary Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Wang, Shengjia Wang, Zhiguo Zhao, Xi-Le Shen, Xiaojing Sichuan Univ Coll Math Chengdu 610064 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 610054 Peoples R China

Dictionary learning (DL) is a pivotal task in machine learning and signal processing, involving extracting representative features from a given dataset. However, conventional DL models are known to be highly sensitive to outliers. To circumvent this issue, we introduce a new and robust DL model based on unbalanced optimal transport (UOT). Compared to DL models based on conventional robust distances and the Wasserstein distance, our model not only captures and leverages the structural information within the data but also demonstrates strong resilience to outliers. By employing the structure of the proposed robust DL model, we develop a novel hybrid block coordinate descent (BCD) algorithm. The proposed algorithm maintains computational tractability by exploiting special block structures of the subproblems. In addition, we establish the convergence of our algorithm without the Lipschitz smooth condition. Through extensive experimentation, we validate our theoretical results and demonstrate the effectiveness of the proposed method on synthetic data, MNIST data, Olivetti faces dataset, and hyperspectral images (HSIs) datasets.

关键词： Robustness Vectors Convergence signal processing algorithms Computational modeling Training Noise Mathematical models Learning systems Feature extraction Block coordinate descent (BCD) Bregman distance robust dictionary learning (DL) unbalanced optimal transport (UOT)

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Time Scale Network: An Efficient Shallow Neural Network for Time Series Data in Biomedical Applications

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第1期19卷 129-139页

作者： Meyer, Trevor Shultz, Camden Dehak, Najim Moro-Velazquez, Laureano Irazoqui, Pedro Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

Time series data is often composed of information at multiple time scales, particularly in biomedical data. While numerous deep learning strategies exist to capture this information, many make networks larger, require more data, are more demanding to compute, and are difficult to interpret. This limits their usefulness in real-world settings facing even modest computational or data constraints and can further complicate their translation into real-time processing or edge device applicaitons. We present a minimal, computationally efficient Time Scale Network combining the translation and dilation sequence used in discrete wavelet transforms with traditional convolutional neural networks and back-propagation. The network simultaneously learns features at many time scales for sequence classification with significantly reduced parameters and operations. We demonstrate advantages in Atrial Dysfunction detection including: superior accuracy-per-parameter and accuracy-per-operation, fast training and inference speeds, and visualization and interpretation of learned patterns in atrial dysfunction detection on ECG signals. We also demonstrate impressive performance in seizure prediction using EEG signals, where our network isolated a few time scales that could be strategically selected to achieve 90.9% accuracy using only 1,133 active parameters and consistently converged on pulsatile waveform shapes. This method does not rest on any constraints or assumptions regarding signal content and could be leveraged in any area of time series analysis dealing with signals containing features at many time scales.

关键词： Transforms signal processing algorithms Time-frequency analysis Feature extraction Accuracy signal processing Computer architecture Efficiency wavelet shallow NN biomedical

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Robust Multichannel Decorrelation via Tensor Einstein Product

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 275-291页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Liu, Guannan San Jose State Univ Dept Appl Data Sci San Jose CA 95192 USA Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA Yuan Ze Univ Innovat Ctr AI Applicat Chungli 32003 Taiwan

Decorrelation of multichannel signals has played a crucial preprocessing role (in prewhitening and orthogonalization) for many signal processing applications. Classical decorrelation techniques can only be applied for signal vectors. Nonetheless, many emerging big-data and sensor-network applications involve signal tensors (signal samples required to be arranged in a tensor form of arbitrary orders). Meanwhile, the existing tensor-decorrelation methods have serious limitations. First, the correlation-tensors have to be of certain particular orders. Second, the unrealistic assumption of the specific signal-tensor form, namely the canonical polyadic (CP) form, is made. Third, the correlation-tensor has to be full-rank or an extra preprocessor based on principal component analysis is required for any non-full-rank correlation tensor. To remove the aforementioned impractical limitations, we propose a novel robust approach for high-dimensional multichannel decorrelation, which can accommodate signal tensors of arbitrary orders, forms, and ranks without any need of extra preprocessor. In this work, we introduce two new tensor-decorrelation algorithms. Our first new algorithm is designed to tackle full-rank correlation-tensors and our second new algorithm is designed to tackle non-full-rank correlation-tensors. Meanwhile, we also propose a new parallel-computing paradigm to accelerate our proposed new tensor-decorrelation algorithms. To demonstrate the applicability of our proposed new scheme, we also apply our proposed new tensor-decorrelation approach to pre-whiten the tensor signals and analyze the corresponding convergence-speed and misadjustment performances of the tensor least-mean-squares (TLMS) filter. Finally, we assess the computational- and memory-complexities of our proposed new algorithms by simulations over both artificial and real data. Simulation results show that our proposed new multichannel-decorrelation algorithms outperform the existing tensor-decorrel

关键词： Tensors Accuracy Computational modeling Simulation signal processing algorithms Estimation Filtering algorithms Vectors Decorrelation Convergence Tensor signal processing Einstein product multichannel decorrelation correlation tensor whitening eigenspread

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Polynomial Graphical Lasso: Learning Edges From Gaussian Graph-Stationary signals

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1153-1167页

作者： Buciulea, Andrei Ying, Jiaxi Marques, Antonio G. Palomar, Daniel P. King Juan Carlos Univ Dept Signal Theory & Commun Madrid 28933 Spain Hong Kong Univ Sci & Technol Dept Math Kowloon Clear Water Bay Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Clear Water Bay Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Decis Analyt Kowloon Clear Water Bay Hong Kong Peoples R China

This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian and stationary on the graph, enabling the development of a graph learning formulation that combines the strengths of graphical lasso with a more encompassing model. Specifically, we assume that the precision matrix can take any polynomial form of the sought graph, allowing for increased flexibility in modeling nodal relationships. Given the inherent complexity and nonconvexity of the optimization problem, we (i) propose a low-complexity algorithm that alternates between estimating the graph and precision matrices, and (ii) characterize its convergence. We evaluate the performance of PGL through comprehensive numerical simulations using both synthetic and real data, demonstrating its superiority over several alternatives. Overall, this approach presents a significant advancement in graph learning and holds promise for various applications in graph-aware signal analysis and beyond.

关键词： Polynomials Covariance matrices Sparse matrices Topology Optimization Filters Convergence signal processing algorithms Correlation Vectors Network-topology inference graph learning Gaussian signals graph-stationary signals covariance estimation

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