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Adaptive Beam Tracking in 5G/6G mmWave Networks: A Clustered Federated Learning Approach

IEEE ACCESS 2025年 13卷 70705-70720页

作者： Ali, Amjad Koucheryavy, Yevgeni HSE Univ Telecommun Res & Dev Inst MIEM Moscow 123458 Russia

Millimeter wave (mmWave, 30-100 GHz) communication is essential for meeting the high data throughput demands of 5G/6G networks. However, mmWave signals are highly susceptible to attenuation and blockage, necessitating directional beamforming antennas and efficient beam tracking algorithms. Traditional machine learning-based approaches, such as centralized learning (CL) and federated learning (FL), face significant challenges. While CL achieves fast convergence, it suffers from high computational costs and privacy concerns. Conversely, FL addresses these issues by enabling distributed model training but struggles with slow convergence and suboptimal accuracy due to data heterogeneity. To overcome these limitations, this paper introduces a novel clustered federated learning (CFL) framework for beam tracking. CFL leverages the benefits of FL while grouping users with similar data distributions, enabling the training of a single model per cluster. This approach reduces communication overhead, accelerates training, and improves accuracy. We analyze key attributes influencing user clustering and their impact on learning efficiency. Numerical results demonstrate that CFL significantly outperforms traditional CL and FL methods, achieving an 18% accuracy improvement over FL (specifically, the FedAvg algorithm) and an 11% enhancement over CL. These findings highlight the potential of CFL for enhancing beam tracking in mmWave systems, offering a more adaptive and privacy-preserving solution for future wireless networks.

关键词： Millimeter wave communication Training Accuracy Data models Radar tracking Adaptation models Tracking Federated learning Convergence clustering algorithms Millimeter wave centralized learning federated learning clustered federated learning beam tracking ray-tracing channel prediction FedAvg

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Gaussian Mixture Model-Based Vector Approach to Real-Time Three-Dimensional Path Planning in Cluttered Environment

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IEEE ACCESS 2025年 13卷 8077-8091页

作者： Tullu, Abera Cho, Yunsang Ko, Sangho Korea Aerosp Univ Dept Smart Air Mobil Goyang Si 10540 Gyeonggi Do South Korea

This work presents an obstacle-free three-dimensional (3D) path planning algorithm for unmanned aerial vehicles (UAV) navigating in cluttered environments. Gaussian mixture model (GMM), a class of unsupervised machine learning, is employed for environment perception based on a proposed vector approach to obstacle-free path planning. GMM circumscribes an obstacle by an ellipsoidal surface defined by its eigenvectors and eigenvalues. The eigenstructure specifies a region occupied by the obstacle so that the path planner re-routes the path whenever the UAV comes closer to this region. The proposed path planner is verified to bypass the challenges and limitations of computationally lightweight path planners such as artificial potential fields. The path planner is also compared with rapidly exploring random tree and shows better performance in optimal path and low computational time. The performance of the proposed path planner is validated in a simulated environment filled with obstacles of various sizes, shapes, and orientations.

关键词： Autonomous aerial vehicles Path planning Navigation Ellipsoids Three-dimensional displays Vectors Shape Gaussian distribution Covariance matrices clustering algorithms Expectation maximization Gaussian mixture model obstacle avoidance path planning unmanned aerial vehicles

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Classified Identification and Estimation of Behind-the-Meter Distributed Photovoltaic Panels Using High-Resolution Aerial Imagery

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 1604-1612页

作者： Li, Kangping Gong, Mingkai Li, Zhenghui Huang, Chunyi Shanghai Jiao Tong Univ Coll Smart Energy Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Noncarbon Energy Convers & Utilizat Inst Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Minist Educ Key Lab Control Power Transmiss & Convers Shanghai 200240 Peoples R China

The continuously increasing penetration of behind-the-meter distributed photovoltaics (PV) proposes significant challenges to the operation of distributed network. Identifying the distributed PV using high-resolution aerial image is a promising and low-cost way to enhance the visibility of distributed PV. Existing studies typically establish a unified identification model, which cannot achieve satisfactory identification performance for different types of distributed PV in practice. To this end, this paper proposes a classified identification and estimation method to accurately acquire the location and size of the installed PV panels within a wide area. Firstly, K-means algorithm is adopted to cluster PV panel images with different size and installation scenarios. For each cluster, U-net is then used to build identification models where focal loss is used as the loss function for better identification of tiny-size panels. Secondly, density-based clustering algorithms and right-angle polygon fit algorithm are employed to optimize the identification results. Finally, the size of PV panels is estimated based on the segmentation masks. The effectiveness of the proposed method has been validated on a real-world aerial imagery dataset.

关键词： Image color analysis Feature extraction Image segmentation Shape Histograms clustering algorithms Distributed photovoltaics behind-the-meter deep learning clustering aerial imagery aerial imagery

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Extended DL Coverage in SAGIN: Cell Association and Resource Allocation With Beam Hopping LEO

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 6014-6028页

作者： Eberechukwu, N. Paulson Onyekwelu, Michael Yoon, Dongweon Hanyang Univ Dept Elect Engn Seoul 04763 South Korea

Realizing the potential of low-Earth orbit (LEO)-based space-air-ground integrated networks requires mitigating challenges associated with LEO satellites, such as limited on-board resources, extended downlink (DL) coverage, temporal variability, and dynamic traffic demand. To mitigate these challenges, an efficient resource allocation (RA) system for beam-hopping can be designed. In this article, we address two practical issues in multidimensional RA for beam-hopping LEO under mixed channel conditions. First, we propose a new clustering algorithm for user equipment cell associations. Second, we decompose the RA problem into three subproblems: 1) time-frequency;2) power;and 3) dynamic RA, and systematically address each subproblem to develop a comprehensive RA approach. We introduce a heuristic approach for time-frequency RA, achieving a good balance between performance and computational complexity. We extend the proposed algorithm to a practical dynamic RA scenario, where the channel parameters are unknown random variables throughout the next time slots, which makes the optimization problem an intrinsically stochastic problem. In addition, throughput balancing for several time slots becomes a dynamic process. Then, we use sequential convex approximations for power allocation and dynamic programming for dynamic RA. Numerical results show that the proposed algorithms outperform baseline methods in delay, capacity (the theoretical maximum data rate), and throughput (the actual data rate achieved).

关键词： Heuristic algorithms Low earth orbit satellites Satellites Space-air-ground integrated networks Resource management Dynamic scheduling clustering algorithms Optimization Computer architecture Satellite broadcasting Cell association low-Earth orbit (LEO) satellite optimization resource allocation (RA) space-air-ground integrated network (SAGIN)

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Illicit Social Accounts? Anti-Money Laundering for Transactional Blockchains

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 391-404页

作者： Song, Jie Zhang, Sijia Zhang, Pengyi Park, Junghoon Gu, Yu Yu, Ge Northeastern Univ Software Coll Shenyang Liaoning Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang Liaoning Peoples R China

recent years, blockchain anonymity has led to more illicit accounts participating in various money laundering transactions. Existing studies typically detect money laundering transactions, known as AML (Anti-money Laundering), through learning transaction features on transaction graphs of transactional blockchains. However, transaction graphs fail to represent the accounts' social features within transactional organizations. Account graphs reveal such features well, and detecting illicit accounts on account graphs provides a new perspective on AML. For example, it helps uncover illegal transactions whose transaction features are not distinct in transaction graphs, with a loose assumption that illicit accounts are likely involved in illegal transactions. In this paper, we propose a Social Attention Graph Neural Network ( SGNN ) on account graphs converted from transaction graphs. To detect illicit accounts, SGNN learns the social features on two sub-graphs, a heterogeneous graph and a hypergraph, extracted from the account graph, and fuses these features into account attribute vectors through attention. The experimental results on the Elliptic++ dataset demonstrate SGNN's advances. It outperforms the best baseline by 14.18% in precision, 7.37% in F1 score, 0.96% in accuracy, and 0.64% in recall when detecting illicit accounts on account graphs, as well as detects 20.3% more recall of illegal transactions through these illicit accounts than state-of-the-art methods based on transaction graphs when the mappings between illegal transactions and illicit accounts are provided. Moreover, thanks to social features, SGNN has a novel capability that works under many account scales and activity degrees. We release our code on https:// ***/CloudLab-NEU/SGNN.

关键词： Blockchains Feature extraction Automated machine learning Law Bitcoin Graph neural networks Vectors Machine learning algorithms Data models clustering algorithms Blockchain anti-money laundering social features anonymity cryptocurrency graph neural network attention mechanism

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Scheduling Single-Arm Cluster Tools With an Equipment Front-End Module Subject to Wafer Residency Time Constraints

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9544-9558页

作者： Huang, Baoying Wu, Naiqi Qiao, Yan Guo, Weiwen Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Macau Peoples R China Hitachi Bldg Technol Guangzhou Co Ltd Guangzhou 510670 Peoples R China

Semiconductor manufacturing widely employs cluster tools that comprise three critical components: a vacuum module (VM), a loadlock module (LLM), and an equipment front-end module (EFEM). In operating such tools, LLM plays a pivotal role. Additionally, wafer fabrication in VM faces strict wafer residency time constraints, making the coordination among the modules particularly challenging. This paper addresses the cyclic scheduling problem of single-arm cluster tools with EFEM and wafer residency time constraints. It focuses on the tool efficiency in module cooperation and schedulability. We explore cooperative strategies for the robot operating within VM to access loadlocks, elucidating the influence of EFEM and LLM on VM. Based on these strategies, we establish necessary and sufficient conditions for the existence of a feasible periodic schedule, providing a foundational basis for the systematic analysis. For schedulable scenarios, we derive an efficient algorithm to identify the feasible and optimal schedule in terms of tool cycle time. The performance and efficiency of the proposed algorithm are validated through experimental verification. Note to Practitioners-In wafer fabs, single-arm cluster tools with EFEM are widely adopted. Studies that neglect the impact of EFEM on scheduling cluster tools are often inapplicable to real-world scenarios. Wafer residency time constraints are essential for maintaining wafer quality. As VM and EFEM operate in different pressure environments, the cooperation via LLM makes it challenging to meet residency time constraints. To address this problem, this paper analyzes the impact of cooperative strategies on the tool performance, and the influence of EFEM and LLM on VM as well. Under these strategies, we examine the schedulability conditions, helping engineers understand how processing parameters affect scheduling feasibility. If a feasible schedule exists, an efficient algorithm is derived to find an optimal schedule. Experimental

关键词： Optimal scheduling Video recording Job shop scheduling Time factors Robot kinematics clustering algorithms Schedules Loading Transportation Petri nets Semiconductor manufacturing cluster tools scheduling

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Polynomial Phase Signal Denoising via Sparse Representations Over a SMAF-Based Dictionary Learning Algorithm

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IEEE ACCESS 2025年 13卷 38268-38281页

作者： Ou, Guojian Zeng, Chenping Dong, Jiaqiang Han, Die Xichang Univ Coll Informat Technol Xichang 615000 Sichuan Peoples R China Key Lab Sichuan Univ Digital Transformat Liangshan Xichang 615000 Peoples R China

In this paper, we address the problem of denoising polynomial phase signals (PPS) by removing additive white Gaussian noise. Our approach is based on sparse representation using a trained dictionary, which is obtained through the secondary moving average filtering (SMAF) dictionary learning algorithm. The proposed algorithm consists of two main steps: first, we utilize K-SVD or other dictionary learning algorithms to obtain the initial trained dictionary;second, we apply the SMAF method to modify the dictionary atoms, enabling them to behave more smoothly and effectively reducing the noise present in the atomic structures. Consequently, the signal-to-noise ratio (SNR) of the reconstructed signal using sparse representation over the refined dictionary is significantly improved compared to K-SVD and RLS-DLA. To achieve optimal denoising effectiveness, we first estimate the SNR of the PPS using our proposed SNR estimation algorithm. Based on the estimated SNR, we then determine the number of samples for the moving average filter impulse response.

关键词： Dictionaries Machine learning Atoms Signal to noise ratio Noise reduction Filtering Low-pass filters Filtering theory clustering algorithms Noise Polynomial phase signal K-SVD dictionary learning secondary moving average filtering

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Unconstrained Fuzzy C-Means Algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第5期47卷 3440-3451页

作者： Nie, Feiping Zhang, Runxin Yu, Weizhong Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Shaanxi Peoples R China China Telecom Corp Ltd Inst Artificial Intelligence TeleAI Beijing 100033 Peoples R China

Fuzzy C-Means algorithm (FCM) is one of the most commonly used fuzzy clustering algorithm, which uses the alternating optimization algorithm to update the membership matrix and the cluster center matrix. FCM achieves effective results in clustering tasks. However, due to many constraints, the objective function is inconvenient to optimize directly and is prone to converges to a suboptimal local minimum, which affects the clustering performance. In this paper, we propose a minimization problem equivalent to FCM. Firstly, we use the optimal solution when fixing the cluster center matrix to replace the membership matrix, transforming the original constrained optimization problem into an unconstrained optimization problem, thus reducing the number of variables. We then use gradient descent instead of alternating optimization to solve the model, so we call this model UC-FCM. Extensive experimental results show that UC-FCM can obtain better local minimum and achieve superior clustering performance compared to FCM under the same initialization. Moreover, UC-FCM is also competitive compared with other advanced clustering algorithms.

关键词： clustering algorithms Optimization Linear programming Convergence Time complexity Symbols Noise Vectors Standards Sensitivity Fuzzy C-Means gradient descent membership matrix local minimum

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Two-Dimensional Balanced Partitioning and Efficient Caching for Distributed Graph Analysis

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第2期36卷 133-149页

作者： Lin, Shuai Wang, Rui Li, Yongkun Xu, Yinlong Lui, John C. S. Univ Sci & Technol China Hefei 230026 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China Univ Sci & Technol China AnHui Prov Key Lab High Performance Comp Hefei 230026 Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

Distributed graph analysis usually partitions a large graph into multiple small-sized subgraphs and distributes them into a cluster of machines for computing. Therefore, graph partitioning plays a crucial role in distributed graph analysis. However, the widely used existing graph partitioning schemes balance only in one dimension (number of edges or vertices) or incur a large number of edge cuts, so they degrade the performance of distributed graph analysis. In this article, we propose a novel graph partition scheme BPart and two enhanced algorithms BPart-C and BPart-S to achieve a balanced partition for both vertices and edges, and also reduce the number of edge cuts. Besides, we also propose a neighbor-aware caching scheme to further reduce the number of edge cuts so as to improve the efficiency of distributed graph analysis. Our experimental results show that BPart-C and BPart-S can achieve a better balance in both dimensions (the number of vertices and edges), and meanwhile reducing the number of edge cuts, compared to multiple existing graph partitioning algorithms, i.e., Chunk-V, Chunk-E, Fennel, and Hash. We also integrate these partitioning algorithms into two popular distributed graph systems, KnightKing and Gemini, to validate their impact on graph analysis efficiency. Results show that both BPart-C and BPart-S can significantly reduce the total running time of various graph applications by up to 60% and 70%, respectively. In addition, the neighbor-aware caching scheme can further improve the performance by up to 24%.

关键词： Partitioning algorithms Computational modeling Synchronization Costs Analytical models Social networking (online) Reviews Load modeling clustering algorithms Blogs Distributed graph systems graph partition graph algorithms graph processing

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Distributed Online Generalized Nash Equilibrium Learning in Multi-Cluster Games: A Delay-Tolerant Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Liu, Bingqian Wen, Guanghui Fang, Xiao Huang, Tingwen Chen, Guanrong Southeast Univ Sch Math Dept Syst Sci Lab Secur Operat & Control Intelligent Autonomous Nanjing 211189 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

This paper addresses the problem of distributed online generalized Nash equilibrium (GNE) learning for multi-cluster games with delayed function feedback. Specifically, each agent in the game is assumed to be informed of a sequence of local cost functions and constraint functions, which are known to the agent with time-varying delays subsequent to decision-making at each round. The objective of each agent within a cluster is to collaboratively optimize the cluster's cost function, subject to time-varying coupled inequality constraints and local constraint sets over time. Additionally, it is assumed that each agent is required to estimate the decisions of all other agents through interactions with its neighbors, rather than directly accessing the decisions of all agents, i.e., each agent needs to make decisions under partial-decision information. To solve such a challenging problem, a novel distributed online delay-tolerant GNE learning algorithm is developed based upon the primal-dual algorithm with an aggregation gradient mechanism. The system-wise regret and the constraint violation are formulated to measure the performance of the algorithm, demonstrating sublinear growth with respect to the time horizon ${\boldsymbol} T$ under certain conditions. Finally, numerical results are presented to verify the effectiveness of the proposed algorithm.

关键词： Games Delays Cost function Vectors Electronic mail Decision making clustering algorithms Nash equilibrium Time measurement Heuristic algorithms Distributed optimization online multi-cluster game generalized Nash equilibrium time-varying delay

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