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Robust Rate-Splitting and Beamforming for Ultra-Reliable and Low-Latency Communications

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 15571-15585页

作者： Li, Tiantian Zhang, Haixia Guo, Shuaishuai Yuan, Dongfeng Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China Shandong Key Lab Intelligent Commun & Sensing Com Jinan 250061 Peoples R China Shandong Univ Shandong Key Lab Intelligent Commun & Sensing Com Jinan 250061 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Shandong Univ Sch Qilu Transportat Jinan 250061 Peoples R China

To provide satisfying services for ever-emerging mission-critical applications, the ultra-reliable and low-latency communications (URLLC) need novel design to improve the spectrum efficiency and enhance the robustness. To achieve this, we design a robust rate-splitting and beamforming scheme for the downlink multiuser URLLC system in finite blocklength regime under imperfect channel state information at the transmitter (CSIT) acquisition. Rate-splitting is utilized to deal with the complex inter-user interference and improve the spectrum efficiency. Considering the norm-bounded CSIT error model, we formulate a minimum user rate maximization problem to guarantee the URLLC performance requirements by jointly designing the rate-splitting factors and the common/private beamforming vectors. The corresponding constraints are infinite due to the uncertainty of CSIT and the constraint set is also non-convex. To tackle it, we convert the infinite constraints into finite ones utilizing S-Procedure, and transform the original problem into difference of convex (DC) programming. Efficient approaches based on constrained concave convex procedure and Gaussian randomization are proposed to solve the DC programming and generate initial feasible points. Through extensive simulations, the convergence, robustness and effectiveness proprieties of the design are investigated and confirmed. Compared with the baselines, our design can achieve obvious performance improvement for different blocklength and block error rate requirements.

关键词： Ultra reliable low latency communication Array signal processing MISO communication Vectors Receivers Mission critical systems approximation algorithms Robust rate-splitting and beamforming ultra-reliable and low-latency communications (URLLC) multiple-input single-output (MISO) finite blocklength (FBL)

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Approximating weighted completion time via stronger negative correlation

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JOURNAL OF SCHEDULING 2024年第4期27卷 319-328页

作者： Baveja, Alok Qu, Xiaoran Srinivasan, Aravind Rutgers Business Sch Dept Supply Chain Management 100 Rockafeller RdRoom 3151 Piscataway NJ 08854 USA Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Montgomery Blair High Sch Silver Spring MD 20901 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Maryland UMIACS College Pk MD 20742 USA

Minimizing the weighted completion time of jobs in the unrelated parallel machines model is a fundamental scheduling problem. The first (3/2 - c)-approximation algorithm for this problem, for some constant c > 0, was obtained in the work of Bansal et al. (SIAM J Comput, 2021). A key ingredient in this work was the first dependent-rounding algorithm with a certain guaranteed amount of negative correlation. We improve upon this guaranteed amount from 1/108 to 1/27, thus also improving upon the constant c in the algorithms of Bansal et al. and Li (SIAM J Comput, 2020) for weighted completion time. Given the now-ubiquitous role played by dependent rounding in scheduling and combinatorial optimization, our improved dependent rounding is also of independent interest.

关键词： Scheduling Completion time approximation algorithms Dependent rounding

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LSHIM: Low-Power and Small-Area Inexact Multiplier for High-Speed Error-Resilient Applications

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2025年第1期15卷 94-104页

作者： Izadi, Azin Jamshidi, Vahid Shahid Bahonar Univ Kerman Dept Comp Engn Kerman *** Iran

Numerical computations in various applications can often tolerate a small degree of error. In fields such as data mining, encoding algorithms, image processing, machine learning, and signal processing where error resilience is crucial approximate computing can effectively replace precise computing to minimize circuit delay and power consumption. In these contexts, a certain level of error is permissible. Multiplication, a fundamental arithmetic operation in computer systems, often leads to increased circuit delay, power usage, and area occupation when performed accurately by multipliers, which are key components in these applications. Thus, developing an optimal multiplier represents a significant advantage for inexact computing systems. In this paper, we introduce a novel approximate multiplier based on the Mitchell algorithm. The proposed design has been implemented using the Cadence software environment with the TSMC 45nm standard-cell library and a supply voltage of 1.1V. Simulation results demonstrate an average reduction of 31.7% in area, 46.8% in power consumption, and 36.1% in circuit delay compared to previous works. The mean relative error distance (MRED) for the proposed method is recorded at 2.6%.

关键词： approximation algorithms Signal processing algorithms Circuits Accuracy Delays Performance evaluation Power demand Arithmetic Adders Energy consumption Approximate multiplier approximate computing Mitchell's multiplication algorithm energy-consumption area delay

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Robust and Unified Semi-Supervised Unmixing of Hyperspectral Imaging for Linear and Multilinear Models

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IEEE ACCESS 2025年 13卷 53140-53158页

作者： Campos-Delgado, Daniel Ulises Nicolas Mendoza-Chavarria, Juan Gutierrez-Navarro, Omar Quintana-Quintana, Laura Leon, Raquel Ortega, Samuel Fabelo, Himar Lopez, Carlos Lejeune, Marylene Callico, Gustavo M. Univ Autonoma San Luis Potosi Fac Ciencias San Luis Potosi 78295 Mexico Univ Autonoma Aguascalientes Biomed Engn Dept Aguascalientes 20100 Mexico Univ Las Palmas Gran Canaria Inst Appl Microelect Las Palmas Gran Canaria 35001 Spain Norwegian Inst Food Fisheries & Aquaculture Res NO N-9019 Tromso Norway Fdn Canaria Inst Invest Sanitaria Canarias FIISC Las Palmas Gran Canaria 35012 Spain Hosp Tortosa Verge Cinta Tortosa 43500 Spain Univ Rovira i Virgili Dept Med & Cirurgia Tarragona 43003 Spain Univ Rovira i Virgili Dept Ciencies Med Basiques Tarragona 43003 Spain

The spectral unmixing paradigm is an important analysis tool for hyperspectral (HS) images which allows one to decompose the 2D spatial information from the basic spectral signatures or end-members. In this work, we introduce a semi-supervised perspective for spectral unmixing, where some end-members are known a priori, while the rest are estimated from the HS image. The proposal is relevant in unmixing scenarios where there is only available partial information of end-members, or when the known end-members are not fully representative of the scene. Our formulation simultaneously addresses linear and multilinear mixing models in a unified fashion. The proposed algorithms are referred as ESSEAE (Extended Semi-Supervised End-members and Abundance Extraction) for the linear model, and NESSEAE (Non-linear Extended Semi-Supervised End-members and Abundance Extraction) for the multilinear one. The estimation process is presented as a weighted optimal approximation problem with regularization terms for abundances, end-members and sparse noise components, which is solved by a cyclic coordinate descent optimization (CCDO) scheme. In this work, we derive closed-solutions at each step of the CCDO scheme, and just for the multilinear model, the end-members estimation involves a gradient descent scheme with optimal linear search. We validate first our contributions with synthetic HS images that include Gaussian and sparse noise components to evaluate their robustness, and compare them with supervised and unsupervised perspectives. In addition, we validated the linear scheme with a breast histological sample, and the multilinear approach with the Urban dataset. The use of two datasets from different fields guarantees the generalizability of the proposed formulation. In general, our semi-supervised spectral unmixing schemes provide accurate and robust results with a fast computational time, and as expected, present an overall performance in between the supervised and unsupervised

关键词： Noise Optimization Vectors Libraries Hyperspectral imaging Estimation Symmetric matrices Spatial coherence approximation algorithms Sparse matrices linear unmixing nonlinear unmixing semi-supervised approach optimization

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Robust Distributed Clustering With Redundant Data Assignment

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第4期71卷 2888-2908页

作者： Bulusu, Saikiran Gandikota, Venkata Mazumdar, Arya Rawat, Ankit Singh Varshney, Pramod K. Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Syracuse Univ Elect Engn & Comp Sci Dept Syracuse NY 13202 USA Univ Calif San Diego Halioglu Data Sci Inst HDSI San Diego CA 92093 USA Google Res NY New York NY 10011 USA

In this work, we present distributed clustering algorithms that can handle large-scale data across multiple machines in the presence of faulty machines. These faulty machines can either be straggling machines that fail to respond within a stipulated time or Byzantines that send arbitrary responses. We propose redundant data assignment schemes that enable us to obtain clustering solutions based on the entire dataset, even when some machines are stragglers or adversarial in nature. Our proposed robust clustering algorithms generate a constant factor approximate solution in the presence of stragglers or Byzantines. We also provide various constructions of the data assignment scheme that provide resilience against a large fraction of faulty machines. Simulation results show that the distributed algorithms based on the proposed assignment scheme provide good-quality solutions for a variety of clustering problems.

关键词： Clustering algorithms Costs Distributed databases Resilience Cost function Delays Computational modeling Computational efficiency approximation algorithms Simulation Distributed clustering coded computations Byzantine-resilient straggler-resilient

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Programmatic Imitation Learning From Unlabeled and Noisy Demonstrations

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第6期9卷 4894-4901页

作者： Xin, Jimmy Zheng, Linus Rahmani, Kia Wei, Jiayi Holtz, Jarrett Dillig, Isil Biswas, Joydeep Univ Texas Austin Austin TX 78712 USA Robert Bosch LLC Farmington Hills MI 48331 USA

Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several well-known limitations: they 1) require large amounts of training data, 2) are hard to interpret, and 3) are hard to refine and adapt. There is an emerging interest in Programmatic Imitation Learning (PIL), which offers significant promise in addressing the above limitations. In PIL, the learned policy is represented in a programming language, making it amenable to interpretation and adaptation to novel settings. However, state-of-the-art PIL algorithms assume access to action labels and struggle to learn from noisy real-world demonstrations. In this paper, we propose Plunder, a novel PIL algorithm that addresses these shortcomings by synthesizing probabilistic programmatic policies that are particularly well-suited for modeling the uncertainties inherent in real-world demonstrations. Our approach leverages an EM loop to simultaneously infer the missing action labels and the most likely probabilistic policy. We benchmark Plunder against several established IL techniques, and demonstrate its superiority across five challenging imitation learning tasks under noise. Plunder policies outperform the next-best baseline by 19% and 17% in matching the given demonstrations and successfully completing the tasks, respectively.

关键词： Task analysis Trajectory Probabilistic logic Noise measurement Training data Noise approximation algorithms Computer science formal languages machine learning programming representation learning robot learning robot programming

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A Radar Signal Deinterleaving Method Based on Enhanced Sparse Subspace Clustering

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 2956-2972页

作者： Guo, Qiang Huang, Shuai Liu, Li-Chao Kaliuzhnyi, Mykola Douplii, Stepan Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Kharkiv Natl Univ Radio Elect Adv Res Lab UA-61166 Kharkov Ukraine Univ Munster Ctr Informat Technol D-48149 Munster Germany

In the modern information battlefield, radar signal deinterleaving plays a pivotal role in perceiving the electromagnetic spatial situation and grasping the initiative of the battle. The increasingly complex electromagnetic environment of modern battlefields leads to serious feature distribution space overlap of radar signal parameters, resulting in limited intraclass aggregation and interclass separability. Therefore, this article introduces the concept of a sparse subspace and proposes a radar signal deinterleaving method based on enhanced sparse subspace clustering. The generalized orthogonal matching pursuit algorithm is used in this study to construct a complex network on the basis of the self-expressiveness property among radar pulses in the same subspace, reducing the connection of pulses from different radars and thus decreasing computational complexity. The graph segmentation concept is incorporated into this algorithm, and Laplacian spectrum decomposition is employed to mine the geometric correlation from radar pulse signals. This approach enables efficient radar signal deinterleaving under nonideal conditions. Simultaneously, an algorithm based on an energy-corrected splitting threshold is proposed to calculate the number of cluster centers accurately. The proposed method effectively addresses the deinterleaving error issue caused by overlapping or missing working state parameters in intercepted complex radar pulse signals, achieving a deinterleaving accuracy of 94.74% for ten interleaved radar pulse signals with severe parameter feature overlap.

关键词： Radar Clustering algorithms Matching pursuit algorithms Complex networks approximation algorithms Electromagnetics Radar countermeasures Computational complexity Accuracy Spaceborne radar Complex network graph segmentation parameter feature overlap radar signal deinterleaving sparse subspace clustering

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One-Way Wave-Equation Migration Based on Jacobi-Anger Expansion in Arbitrarily Lateral Varying Media

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Revelo, Daniel E. Pestana, Reynam C. Fed Univ Bahia UFBA Geosci Inst BR-40170290 Salvador BA Brazil SENAI CIMATEC Supercomp Ctr BR-41650010 Salvador BA Brazil INCT GP CNPq BR-40170115 Salvador BA Brazil

Seismic depth migration by downward continuation using conventional one-way wave-equation operators has three main shortcomings: handling evanescent waves, imaging large angles, and stability of the one-way propagator. Conventional one-way wave-equation propagators have been extensively constructed using approximation theories, such as Taylor series (TS) and other expansions. Although in these, a new operator is calculated at each lateral point of the grid, difficulties persist due to the assumption that the medium is locally homogeneous. Consequently, these propagators exhibit significant weaknesses in imaging complex media. To alleviate these drawbacks, we propose a stable and affordable approach to the one-way wave-equation depth migration algorithm. First, we use a spectral projector (SP) to suppress evanescent modes in an arbitrarily laterally varying velocity model. Next, we apply a coupled Schulz iteration scheme to the Helmholtz operator to obtain the square-root operator. Finally, to address the issue of accurately constructing the extrapolation operator while ensuring stability, we introduce a novel algorithm to approximate the exponential matrix operator using the Jacobi-Anger (JA) expansion. This approximation enables the one-way wave-equation propagator to be constructed solely through matrix multiplication, a feature that facilitates efficient implementation and execution. Through an impulse response test, we demonstrate that our proposed migration algorithm is more accurate than the one using TS expansion and is effective for large angles in media with pronounced lateral velocity variations. We also use models with complex subsurface structures and field data to demonstrate that our approach performs better at imaging intricate and fine-scale features compared to traditional one-way migration methods.

关键词： Propagation Jacobian matrices Extrapolation Imaging Accuracy Taylor series Media Finite difference methods Filtering theory approximation algorithms Coupled Schulz iteration scheme Helmholtz operator Jacobi-Anger (JA) expansion matrix multiplication one-way wave-equation migration (WEM) spectral projector (SP)

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QUBO Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem

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IEEE ACCESS 2025年 13卷 47086-47098页

作者： Guney, Evren Ehrenthal, Joachim Hanne, Thomas MEF Univ Dept Ind Engn TR-34396 Istanbul Turkiye Univ Appl Sci & Arts Northwestern Switzerland FHNW Inst Business Informat Syst Sch Business CH-5210 Windisch Switzerland

The Multi-Knapsack Problem (MKP) is a fundamental challenge in operations research and combinatorial optimization. Quantum computing introduces new possibilities for solving MKP using Quadratic Unconstrained Binary Optimization (QUBO) models. However, a key challenge in QUBO formulations is the selection of penalty parameters, which directly influence solution feasibility and algorithm performance. In this work, we develop QUBO formulations for two MKP variants-the Multidimensional Knapsack Problem (MDKP) and the Multiple Knapsack Problem (MUKP)-and provide an algebraic characterization of their penalty parameters. We systematically evaluate their impact through quantum simulation experiments and compare the performance of the two leading quantum optimization approaches: Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, alongside a state-of-the-art classical solver. Our results indicate that while classical solvers remain superior, careful tuning of penalty parameters has a strong impact on quantum optimization outcomes. QAOA is highly sensitive to parameter choices, whereas quantum annealing produces more stable results on small to mid-sized instances. Further, our results reveal that MDKP instances can maintain feasibility at penalty values below theoretical bounds, while MUKP instances show greater sensitivity to penalty reductions. Finally, we outline directions for future research in solving MKP, including adaptive penalty parameter tuning, hybrid quantum-classical approaches, and practical optimization strategies for QAOA, as well as real-hardware evaluations.

关键词： Optimization Quantum computing Logic gates Quantum annealing Annealing Qubit Noise approximation algorithms Tuning Quantum state Multi-dimensional knapsack problem multiple knapsack problem quadratic unconstrained binary optimization quantum annealing quantum approximate optimization algorithm penalty parameters

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Asynchronous Parallel Large-Scale Gaussian Process Regression

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第6期35卷 8683-8694页

作者： Dang, Zhiyuan Gu, Bin Deng, Cheng Huang, Heng Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Dept Machine Learning Abu Dhabi U Arab Emirates Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15260 USA

Gaussian process regression (GPR) is an important nonparametric learning method in machine learning research with many real-world applications. It is well known that training large-scale GPR is a challenging task due to the required heavy computational cost and large volume memory. To address this challenging problem, in this article, we propose an asynchronous doubly stochastic gradient algorithm to handle the large-scale training of GPR. We formulate the GPR to a convex optimization problem, i.e., kernel ridge regression. After that, in order to efficiently solve this convex kernel problem, we first use the random feature mapping method to approximate the kernel model and then utilize two unbiased stochastic approximations, i.e., stochastic variance reduced gradient and stochastic coordinate descent, to update the solution asynchronously and in parallel. In this way, our algorithm scales well in both sample size and dimensionality, and speeds up the training computation. More importantly, we prove that our algorithm has a global linear convergence rate. Our experimental results on eight large-scale benchmark datasets with both regression and classification tasks show that the proposed algorithm outperforms the existing state-of-the-art GPR methods.

关键词： Kernel approximation algorithms Training Optimization Gaussian processes Convergence Computational modeling Asynchronous parallel learning big data Gaussian process regression (GPR) random features

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