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Scalable Cell-Free Massive MIMO Networks Using Resource-Optimized Backhaul and PSO-Driven Fronthaul clustering

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 1153-1168页

作者： Ajmal, Mahnoor Tariq, Muhammad Ashar Saad, Malik Muhammad Kim, Sunghyun Kim, Dongkyun Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea

Scalability presents a formidable challenge in traditional Cell-Free (CF) massive Multiple Input Multiple Output (mMIMO) networks, driven by escalating computational demands on access points (APs) and the reliance on a single central processing unit (CPU). To address this, the study proposes a dynamic cooperative clustering (DCC) method, tailored for both backhaul (CPUs-APs) and fronthaul (APs-Users). In the backhaul phase, DCC strategically pairs APs with CPUs using the Kuhn-Munkres algorithm, ensuring equitable resource allocation by considering distance matrices, channel statistics, APs traffic load, and available CPU resources, thereby fairly balancing the distribution of computational load across the CPUs. Subsequently, in the fronthaul phase, the focus is on optimizing the selection of APs for user-centric clusters, using Particle Swarm Optimization (PSO). This optimization aims to maximize the overall sum rate while intelligently managing the inclusion and exclusion of APs within each user-serving cluster. Through extensive simulations, the study highlights the potential of the proposed approach to address scalability concerns in CF-massive MIMO systems, promising improved performance in wireless communication networks. The comparative analysis demonstrates the superiority of the proposed scheme over conventional clustering schemes, consistently delivering better sum rates across various scenarios, with an 18.23% improvement in sum rate and a 30% enhancement in Load Balancing Index (LBI), indicating significantly improved resource distribution and network efficiency.

关键词： Backhaul networks Scalability clustering algorithms Central Processing Unit Wireless communication Vehicle dynamics Heuristic algorithms Backhaul clusters CF-mMIMO fronthaul clusters scalability

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Integrating Fuzzy clustering and Graph Convolution Network to Accurately Identify Clusters From Attributed Graph

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 1112-1125页

作者： Yang, Yue Li, Guodong Li, Dongxu Zhang, Jun Hu, Pengwei Hu, Lun Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830000 Peoples R China

Attributed graph clustering is of significance for an in-depth understanding of the intrinsic organization of complex networks. Recently, owing to the powerful learning capability of deep neural networks, increasing attention has been paid to developing more advanced deep learning approaches for attributed graph clustering. However, these approaches mainly focus on the purpose of obtaining general node representations, followed by clustering as a downstream task in combination with traditional techniques. Intuitively, the clustering performance is further limited due to the lack of achieving the desirable clustering quality in the graph representation learning process. To this end, this paper proposes a novel end-to-end attributed graph clustering model, namely FCGCN, by integrating fuzzy clustering and graph convolution network. FCGCN is trained toward optimizing an unsupervised fuzzy-based clustering objective, which is specifically formulated for precisely evaluating the clustering quality by considering the fuzzy memberships of nodes over clusters. To avoid the generation of undesirable clusters, we introduce a tailored regularization term by alleviating the over-smoothing issue on graph neural networks. By doing so, an explicit connection between graph representation learning and clustering can thus be established, considerably improving the clustering performance. To evaluate the performance of FCGCN, extensive experiments are conducted on a total of five real attributed graph datasets, and the results demonstrate the superiority of FCGCN over several state-of-the-art algorithms in terms of effectiveness and efficiency.

关键词： clustering algorithms Representation learning Graph neural networks Optimization Convolution clustering methods Feature extraction Deep learning Loss measurement Data mining Attributed graph fuzzy clustering graph representation graph neural network

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Examining the Impact of Fingerprint Vector Size on Similarity Determination and clustering Performance of a Pattern-Based Similarity Metric

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IEEE ACCESS 2025年 13卷 51660-51668页

作者： Yaro, Abdulmalik Shehu Maly, Filip Prazak, Pavel Univ Hradec Kralove Fac Informat & Management Dept Informat & Quantitat Methods Hradec Kralove 50003 Czech Republic Ahmadu Bello Univ Dept Elect & Telecommun Engn Zaria 810106 Nigeria

The correlation-based context similarity coefficient (CSC) metric is a pattern-based fingerprint similarity metric that has gained interest in fingerprint database clustering operations. The performance of the traditional distance-based metric in fingerprint vector similarity determination has been known to be influenced by the size of the fingerprint vector. However, as for the correlation-based CSC metric, there is no comprehensive research on how fingerprint vector size affects its performance in similarity determination and subsequently clustering performance. As such, this paper examines the impact of fingerprint vector size on the similarity determination performance of the correlation-based CSC metric with the k-medoids algorithm employed for clustering. The analysis is performed across four synthetic and two experimentally generated fingerprint databases with varying fingerprint vector sizes. The impact analysis is carried out with the clustering algorithm set to generate K is an element of [3, 5] clusters. Additionally, the results are compared against three distance-based metrics: squared Euclidean, Manhattan, and cosine. With silhouette score as the clustering performance metric, the simulation result shows that the size of the fingerprint vector influences the similarity determination performance of the correlation-based CSC metric. Additionally, the number of clusters in which the clustering algorithm is set to generate also contributes to how the correlation-based CSC metric performs in similarity determination. The similarity determination time complexity of the correlation-based CSC metric increases with fingerprint vector size, making efficient clustering more challenging as fingerprint vector sizes increase. For optimal performance, the correlation-based CSC metric is recommended for us as a similarity metric on a database with a fingerprint vector size of N <= 4 and a clustering algorithm configured to generate no more than 3 clusters.

关键词： Fingerprint recognition Vectors Measurement clustering algorithms Databases Measurement uncertainty Accuracy Mathematical models Partitioning algorithms Location awareness clustering context similarity coefficient fingerprint vector size pattern-based metric similarity determination

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Federated Multi-View K-Means clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第4期47卷 2446-2459页

作者： Yang, Miin-Shen Sinaga, Kristina P. Chung Yuan Christian Univ Dept Appl Math Taoyuan 32023 Taiwan

The increasing effect of Internet of Things (IoT) unlocks the massive volume of the availability of Big Data in many fields. Generally, these Big Data may be in a non-independently and identically distributed fashion (non-IID). In this paper, we have contributions in such a way enable multi-view k-means (MVKM) clustering to maintain the privacy of each database by allowing MVKM to be operated on the local principle of clients' multi-view data. This work integrates the exponential distance to transform the weighted Euclidean distance on MVKM so that it can make full use of development in federated learning via the MVKM clustering algorithm. The proposed algorithm, called a federated MVKM (Fed-MVKM), can provide a whole new level adding a lot of new ideas to produce a much better output. The proposed Fed-MVKM is highly suitable for clustering large data sets. To demonstrate its efficient and applicable, we implement a synthetic and six real multi-view data sets and then perform Federated Peter-Clark in Huang et al. 2023 for causal inference setting to split the data instances over multiple clients, efficiently. The results show that shared-models based local cluster centers with data-driven in the federated environment can generate a satisfying final pattern of one multi-view data that simultaneously improve the clustering performance of (non-federated) MVKM clustering algorithms.

关键词： clustering algorithms Federated learning Distributed databases Data models Data privacy Machine learning algorithms Kernel Internet of Things Servers Training data clustering K-means multi-view data multi-view k-means (MVKM) federated learning federated MVKM privacy

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A Cross-Layer FL-Based clustering Protocol to Support Multicast Routing in IoT-Enabled MANETs With CF-mMIMO

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IEEE ACCESS 2025年 13卷 3881-3899页

作者： Amalia, Amalia Pramitarini, Yushintia Perdana, Ridho Hendra Yoga Shim, Kyusung An, Beongku Hongik Univ Grad Sch Dept Software & Commun Engn Sejong 30016 South Korea Hankyong Natl Univ Sch Comp Engn & Appl Math Anseong 17579 Gyeonggi South Korea Hongik Univ Dept Software & Commun Engn Sejong 30016 South Korea

This paper proposes a novel cross-layer federated learning (FL)-based clustering (CFLC) protocol to support multicast routing in internet of things (IoT)-enabled mobile ad hoc networks (MANETs) with cell-free massive multiple-input multiple-output (CF-mMIMO). The proposed CFLC protocol leverages cross-layer and FL approaches to enhance network stability and connectivity by optimizing cluster head (CH) selection and cluster formation. The cross-layer design integrates physical layer information such as mobility (speed and direction), position, channel capacity, and remaining energy, with network layer information (connectivity) to maximize the cost function value for cluster formation. We design the FL model to improve the clustering performance and satisfy future mobile network requirements. Specifically, during the CH selection step, FL can decide which nodes should be elected as CHs or cluster members (CMs) by using classification. In the cluster formation step, FL addresses a regression problem by optimizing the cost function weights for parameters such as mobility similarity, link quality, remaining energy, and channel capacity to decide which CH each node should follow. The simulation results show that the proposed CFLC protocol outperforms the benchmark protocols in terms of connectivity, scalability, and control overhead. Additionally, the results indicate that the CFLC protocol performs particularly well when using the reference point group mobility (RPGM) model, highlighting its advantage over the random waypoint (RWP) mobility model in maintaining network stability and connectivity.

关键词： Protocols Cross layer design Ad hoc networks Routing clustering algorithms Physical layer Ultra reliable low latency communication Cost function Stability criteria Solid modeling Cell-free massive MIMO clustering cross-layer federated learning IoT-enabled mobile ad hoc network

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Multivariate Fuzzy Density-Based Temporospatial clustering of Applications With Noise (MF-DBTSCAN) Algorithm for Detection and Tracking of Multiple Targets by 77-GHz Millimeter Wave Radar Sensor

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IEEE SENSORS JOURNAL 2025年第3期25卷 5731-5744页

作者： Venkatesha, K. Singh, Upasna Vengadarajan, A. Pardhasaradhi, Bethi Cenkeramaddi, Linga Reddy DIAT Dept Comp Sci & Engn Pune 411025 Maharashtra India DRDO Elect & Radar Dev Estab Bengaluru 560093 Karnataka India Univ Agder UiA ICT Dept N-4879 Grimstad Norway

We propose multivariate fuzzy density-based temporospatial clustering of applications with noise (MF-DBTSCAN), a novel clustering algorithm combining multivariate, fuzzy, temporal, and spatial features. The primary objective of the proposed clustering algorithm is to establish well-defined clusters that address the challenges associated with proximity targets, crossing targets, and targets overlapping in range, Doppler, angle and their combination in multitarget environments. This objective is pursued through a thoughtful integration of spatial and temporal features alongside multivariate fuzziness to enhance the association of detections. In this proposed algorithm, range, Doppler and angle of arrival (AoA) in azimuth are used as three fuzzy variables with independent fuzzy membership functions to handle the probabilistic nature of the radar sensor measurements. Multivariate and fuzziness features of the algorithm are used to discern the clusters with discrete features, which eventually can be used to resolve the proximity targets and reduce track fragmentation and spurious tracks. The temporal feature of the algorithm includes dynamic clustering, a time-based minimal detection set during cluster formation, and purging of inactive tracks and false detections. This temporal feature significantly reduces cluster formation time and eliminates the false tracks. Well-formed clusters are fed to the Kalman filter (KF) for multitarget tracking. The proposed algorithm has been successfully validated with experimental data from a 77-GHz millimeter wave (mmWave) radar sensor mounted on a vehicle. Several experiments were carried out, encompassing a range of on-the-road scenarios involving radar mounted on a moving or stationary vehicle, moving and stationary objects, or stationary clutters. The scenario includes various situations where targets are in close proximity, including crossing ranges, having similar Doppler frequencies, close AoA, or a combination of these factors.

关键词： Radar tracking Radar clustering algorithms Target tracking Sensors Millimeter wave communication Radar detection Radar cross-sections Heuristic algorithms Doppler radar 77 GHz advanced driver assistance system (ADAS) density-based clustering method for multidimensional spatiotemporal data (MDST-DBSCAN) density-based spatial clustering of applications with noise (DBSCAN) fuzzy neighborhood-DBSCAN (FN-DBSCAN) millimeter wave (mmWave) radar sensor multivariate fuzzy density-based temporospatial clustering of applications with noise (MF-DBTSCAN) temparospatial

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clustering Algorithm by Boundary Detection Base on Entropy of KNN

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IEEE ACCESS 2025年 13卷 62353-62366页

作者： Ding, Jiaman Yin, Jinyuan Jia, Lianyin Fu, Xiaodong Wang, Hongbin Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Artificial Intelligence Key Lab Yunnan Prov Kunming 650500 Peoples R China

clustering analysis has been widely applied in various fields, and boundary detection based clustering algorithms have shown effective performance. In this work, we propose a clustering algorithm by boundary detection based on entropy of KNN (CBDEK). A border point contains only the nearest neighbors within a specific directional range. Thus, we define entropy of KNN (EK) to accurately identify the boundary of the cluster. Since entropy has the property of measuring uncertainty, it can be used to quantify the possibility that a point is a border point. A lower EK indicates an uneven neighbor distribution, increasing the possibility of the point being a border point. Then, the border points are clustered based on the directional similarity of their nearest neighbors. Specifically, if a border point is a neighbor of another border point and most of their nearest neighbors show directional similarity, they are considered to belong to the same cluster. Furthermore, we assign the label of a border point to the interior points located within the maximum nearest neighbor sub-block of this border point to facilitate an efficient allocation for the remaining points (interior points). In addition, our algorithm incorporates noise mitigation techniques using average distance and box plot analysis. The effectiveness of CBDEK is proven by a comparative evaluation of nine algorithms on 24 datasets.

关键词： clustering algorithms Nearest neighbor methods Entropy Noise Object recognition Shape Vectors Prevention and mitigation Noise measurement Uncertainty Boundary detection box plot clustering directional similarity entropy of KNN

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Hierarchical clustering-Based Algorithm for Deployment Planning of LoRa Gateway

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IEEE ACCESS 2025年 13卷 25837-25857页

作者： Lutfie, Kalam Adhiansyah Purnamasari, Prima Dewi Gunawan, Dadang Enriko, I. Ketut Agung Juwono, Filbert H. Univ Indonesia Dept Elect Engn Depok City 16424 Indonesia Inst Teknol Telkom Purwokerto Dept Telecommun & Elect Engn Purwokerto Indonesia Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

Long-Range Wide Area Network (LoRaWAN) has become a promising communication method for the Internet of Things (IoT) system since it is capable of long-range communication with low power usage. Presently, LoRa gateway (GW) deployment in Indonesia relies on conventional methods, i.e., predicting coverage without incorporating formulas specific to LoRa GW performance or accounting for the area type. This research aims to deploy the LoRa GW using a machine-learning algorithm to cover every sensor demand. In this paper, we simulate, deploy, and measure the signal strength transmission between recommended LoRa GWs and end device (ED) demands to ensure the algorithm works and is usable on each different environment characteristic. The results of these experiments show that the algorithm can generate coordinate point recommendations for the LoRa GW to cover all ED demands in three different characteristic areas.

关键词： LoRaWAN Logic gates Internet of Things clustering algorithms Couplings Wireless communication Actuators Wireless sensor networks Sensitivity Propagation losses LoRa machine learning IoT

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A Spatial Model and Multiobjective Fuzzy Inference-Based clustering Routing Algorithm for Bridge Monitoring

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IEEE SENSORS JOURNAL 2025年第1期25卷 1669-1681页

作者： Yang, Jiguang Huo, Jiuyuan Mu, Cong Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Natl Cryosphere Desert Data Ctr NCDC Lanzhou 730000 Peoples R China

Wireless sensors have the advantages of distributed and flexible deployment, real-time monitoring, multisensor collaboration, self-organization, and self-adaptation. They can be widely used in bridge structural health monitoring systems (BHMSs). At the same time, wireless sensors are still limited by battery life and communication distance;extending the life cycle of wireless sensor networks (WSNs) employing optimal deployment and effective improvement of routing algorithms is a research hotspot for domestic and foreign engineering and academic circles. In this article, a WSN clustering routing algorithm based on bridge spatial model and multiobjective fuzzy inference (BSMOFC) is proposed to solve the problem that the existing research has not considered the spatial characteristics of bridges and the reasonable choice of cluster head (CH) in the clustering routing algorithm. First, a bridge space model is designed to simulate the 3-D deployment of WSN nodes;second, the CH selection process of the WSN clustering routing algorithm is defined as a multiobjective optimization problem, and the multiobjective non-dominated sorted whale optimization algorithm (NSWOA) is used to solve the set of Pareto solutions. All the solutions are defined as the candidate CHs (C-CHs), and then a fuzzy inference system is used to choose the final CHs (F-CHs). Finally, the simulation platform MATLAB completes the performance verification of the BSMOFC algorithm. The simulation results show that the BSMOFC algorithm can effectively extend the WSN lifetime and provide a new practical method for selecting a Pareto optimal solution.

关键词： Wireless sensor networks clustering algorithms Bridges Routing Optimization Monitoring Inference algorithms Wireless communication Fuzzy logic Sensor phenomena and characterization Bridge space model bridge structural health monitoring system (BHMS) fuzzy inference system multiobjective optimization wireless sensor network (WSN)

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Characterization and clustering of rock discontinuity sets:A review

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Journal of Rock Mechanics and Geotechnical Engineering 2025年第2期17卷 1240-1262页

作者： Changle Pu Jiewei Zhan Wen Zhang Jianbing Peng College of Geological Engineering and Geomatics Chang'an UniversityXi'an710054China Key Laboratory of Western China's Mineral Resources and Geological Engineering Ministry of EducationChang'an UniversityXi'an710054China College of Construction Engineering Jilin UniversityChangchun130026China

The characterization and clustering of rock discontinuity sets are a crucial and challenging task in rock mechanics and geotechnical *** the past few decades,the clustering of discontinuity sets has undergone rapid and remarkable ***,there is no relevant literature summarizing these achievements,and this paper attempts to elaborate on the current status and prospects in this ***,this review aims to discuss the development process of clustering methods for discontinuity sets and the state-of-the-art relevant ***,we introduce the importance of discontinuity clustering analysis and follow the comprehensive characterization approaches of discontinuity data.A bibliometric analysis is subsequently conducted to clarify the current status and development characteristics of the clustering of discontinuity *** methods for the clustering analysis of rock discontinuities are reviewed in terms of single-and multi-parameter clustering ***-parameter methods can be classified into empirical judgment methods,dynamic clustering methods,relative static clustering methods,and static clustering methods,reflecting the continuous optimization and improvement of clustering ***,this paper compares the current mainstream of single-parameter clustering methods with multi-parameter clustering *** is emphasized that the current single-parameter clustering methods have reached their performance limits,with little room for improvement,and that there is a need to extend the study of multi-parameter clustering ***,several suggestions are offered for future research on the clustering of discontinuity sets.

关键词： Discontinuity clustering clustering algorithms Discontinuity characterization Orientation analysis Rock mass

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