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Joint Beamforming and Compressed Sensing for Uplink Grant-Free Access

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第9期23卷 10575-10591页

作者： Xia, Guoqing Xiao, Pei Li, Bohan Zhang, Yue Zhou, Huiyu Univ Leicester Sch Engn Leicester LE1 7RH England Univ Surrey Inst Commun Syst ICS 5GIC & 6GIC Guildford GU2 7XH England Ocean Univ China Fac Informat Sci & Engn Qingdao 266100 Peoples R China Inst Commun Syst & Measurement Technol Chengdu 610095 Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England

Compressed sensing (CS)-based techniques have been widely applied in the grant-free non-orthogonal multiple access (NOMA) to a single-antenna base station (BS). In this paper, we consider the multi-antenna reception at the BS for uplink grant-free access for the massive machine type communication (mMTC) with limited channel resources. To enhance the overloading performance of the BS, we develop a general framework for the synergistic amalgamation of the spatial division multiple access (SDMA) technique with the CS-based grant-free NOMA. We derive a closed-form statistical beamforming and a dynamic beamforming scheme for the inter-cluster interference suppression when applying SDMA. Based on this, we further develop a joint adaptive beamforming and subspace pursuit (J-ABF-SP) algorithm for the multiuser detection and data recovery, with a novel sparsity level decision method without the accurate knowledge of the noise level. To further improve the data recovery performance, we propose an interference cancellation-based J-ABF-SP scheme (J-ABF-SP-IC) by using the initial signal estimates generated from the J-ABF-SP algorithm. Illustrative simulations verify the superior user detection and signal recovery performance of our proposed algorithms in comparison with existing CS-based grant-free NOMA techniques.

关键词： NOMA Array signal processing Multiaccess communication Multiuser detection Correlation clustering algorithms Channel estimation mMTC grant-free access beamforming subspace pursuit joint optimisation interference cancellation

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Coordinated Computing Resource Allocation With Efficiency Maximization in Heterogeneous Platoon Edge Network

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15809-15826页

作者： Zhu, Shuya Meng, Kaitao Wang, Rui Li, Deshi Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China UCL Dept Elect & Elect Engn London WC1E 7JE England

Under numerous computation requirements in intelligent traffic, platoons comprised of several connected vehicles are expected to centralize vehicular computation resources, which can provide computing services for surrounding mobile users and thus facilitate the deployment of vehicular edge computing (VEC). Nevertheless, the computing resources in multi-platoon are distributed unevenly, making platoons' resource allocation highly selective, especially for randomly distributed mobile users. Moreover, existing works mainly focus on single-platoon-assisted VEC, which lacks resource coordination among platoons and may result in resource imbalance. Thus, through coordinating computing resource allocation in platoons and base stations (BSs), a coordinated computing resource allocation scheme is proposed in this paper to maximize the utilities of mobile users. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. To address this issue, the efficiency-maximized optimal computing resources allocated from platoons to users are derived in a semi-closed form. Then, to decouple the variables in allocation coordination, the efficiency maximization problem is proven to be equivalent to a low-complexity resource allocation problem oriented for single users. Based on the above results, two algorithms are proposed to convert the NP-hard problem into convex ones, 1) through efficiency-maximized task offloading and backtracking iteratively, a profit efficiency backtracking algorithm is proposed to coordinate computing resource allocation among platoons, and 2) heterogeneous profit efficiency algorithm is proposed to solve the primal problem, where a partition ratio-based efficiency maximization computing resource allocation problem is optimized in heterogeneous edges. Extensive simulation results show that our proposed scheme can improve resource allocation efficiency, task success rates, and service continuity over benchmark scheme

关键词： Resource management Task analysis Computational modeling Partitioning algorithms Heuristic algorithms Backtracking clustering algorithms Vehicular edge computing platoon collaborative task offloading resource allocation coordination

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Masked Topology Convolutional Network for Classification and Segmentation of Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 18-18页

作者： Wang, Falin Ji, Jian Wang, Yuan Li, Jingyang Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

Convolutional neural networks have made significant progress in remote sensing image processing. Convolutional networks mostly model the local information of samples based on pixel features, while ignoring the topological relationship among different categories of ground objects. However, the nonlocal topology can better represent the underlying data structure of the image. In order to make up for the insufficiency of Convolutional neural networks in extracting nonlocal topological information, we propose a new masked topology convolutional network (MTC_Net). First, considering the regionality of different categories of ground objects, we use a clustering algorithm to simply cluster the pixels to extract the most obvious area mask, which can strengthen the boundary information among different ground objects. Second, we take the cluster center point as the representative point of the region and use the graph structure to represent the topological relationship, in addition use graph convolution to further extract the topological structure relationship among different categories of ground objects. Finally, we use convolution modules and multilevel feature attention modules to capture important local and global contextual semantic information and reduce category confusion. We conduct a great deal of experiments on four (two classification and two segmentation) public datasets, and the proposed MTC_Net achieves excellent classification and segmentation performance.

关键词： Feature extraction Convolutional neural networks Data mining Image segmentation Topology clustering algorithms Network topology Classification and segmentation graph convolutional network (GCN) remote sensing images topological structure

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A Novel k-Means Framework via Constrained Relaxation and Spectral Rotation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 15142-15155页

作者： Chen, Jingwei Xie, Shiyu Jiang, Hongyun Yang, Hui Nie, Feiping East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Jiangxi Peoples R China East China Jiaotong Univ Key Lab Adv Control & Optimizat Jiangxi Prov Nanchang 330013 Jiangxi Peoples R China China Railway Conservancy & Hydropower Planning & Nanchang 330029 Jiangxi Peoples R China State Grid Nanchang Elect Power Co Nanchang 330069 Jiangxi Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect IOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China

Owing to its simplicity, the traditional k-means (Lloyd heuristic) clustering method plays a vital role in a variety of machine-learning applications. Disappointingly, the Lloyd heuristic is prone to local minima. In this article, we propose k-mRSR, which converts the sum-of-squared error (SSE) (Lloyd) into a combinatorial optimization problem and incorporates a relaxed trace maximization term and an improved spectral rotation term. The main advantage of k-mRSR is that it only needs to solve the membership matrix instead of computing the cluster centers in each iteration. Furthermore, we present a nonredundant coordinate descent method that brings the discrete solution infinitely close to the scaled partition matrix. Two novel findings from the experiments are that k-mRSR can further decrease (increase) the objective function values of the k-means obtained by Lloyd (CD), while Lloyd (CD) cannot decrease (increase) the objective function obtained by k-mRSR. In addition, the results of extensive experiments on 15 datasets indicate that k-mRSR outperforms both Lloyd and CD in terms of the objective function value and outperforms other state-of-the-art methods in terms of clustering performance.

关键词： Optimization Linear programming clustering algorithms Matrix converters Kernel clustering methods Companies clustering k-means Lloyd heuristic spectral relaxation spectral rotation

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Group'n Route: An Edge Learning-Based clustering and Efficient Routing Scheme Leveraging Social Strength for the Internet of Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第10期23卷 19589-19601页

作者： Magaia, Naercio Ferreira, Pedro Pereira, Paulo Rogerio Muhammad, Khan Del Ser, Javier de Albuquerque, Victor Hugo C. Univ Lusdona COPELABS P-1749024 Lisbon Portugal Univ Sussex Sch Engn & Informat Brighton BN1 9QJ E Sussex England Univ Lisbon Inst Super Tacn INESC ID P-1649004 Lisbon Portugal Sungkyunkwan Univ Sch Convergence Coll Comp & InformatVIS2KNOW Lab Dept Appl Artificial IntelligenceVisual Analyt K Seoul 03063 South Korea TECNALIA Derio 48160 Biscay Spain Univ Basque Country UPV EHU Dept Commun Engn Bilbao 48013 Spain Univ Fed Ceara Dept Teleinformat Engn BR-60750740 Fortaleza Ceara Brazil

The Internet of Vehicles (IoV) is undoubtedly at the core of the future of intelligent transportation. It will prevail over the road ecosystem, and it will have a huge impact on our lives throughout the provision of seamless connectivity among diverse transportation means. For the network to operate efficiently, the data needs to be quickly spread throughout the network, which requires low computational and bandwidth overheads. However, the dynamics of vehicular environments due to frequent node mobility poses many challenges to realize efficient data dissemination. This work addresses this type of problem by proposing a novel clustering algorithm at the edge of the network and an efficient message routing approach, which is known as Group'n Route (GnR). Both mechanisms resort to machine learning and graph metrics that reflect the social relationships between the nodes. Our performance evaluation reveals that the clustering algorithm yields stable results with varying road scenarios, which are becoming an advisable approach in the presence of mobile IoV nodes. Also, the designed routing protocol achieves two orders of magnitude smaller overhead and almost double the delivery rate when it is compared to traditional routing protocols, which thereby justify that the combination of our two proposed clustering and routing methods are a plausible alternative to support IoV communications in real-world setups.

关键词： Routing Routing protocols clustering algorithms Vehicle dynamics Cloud computing Roads Heuristic algorithms Internet of Vehicles edge learning clustering graph theory routing social strength

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PdYOLO: A Lightweight Algorithm for Detecting Peach Fruits Against a Peach Tree Background

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IEEE ACCESS 2024年 12卷 193487-193497页

作者： Zhang, Jiajun Zhang, Jialin Kobayashi, Kazuki Harbin Univ Commerce Fac Comp & Informat Engn Harbin 150028 Peoples R China Shinshu Univ Acad Assembly Nagano 3808553 Japan

Accurate peach fruit detection in a fruit tree environment is of great significance for intelligent harvesting technology and accurate yield prediction. Peaches have a short picking cycle and a concentrated ripening period. However, the backgrounds of peach trees, including trunks, branches, and leaves, contribute to uneven sample distribution. Additionally, the diverse shapes of peach fruits, variations in light, leaf shading, and dense clustering complicate detection. To address these challenges, this study introduces PdYOLO (Peach detection based on YOLOv8s), an innovative peach detection model optimized within the YOLOv8s framework. This model enhances recognition accuracy, reduces algorithm complexity, and satisfies lightweight requirements. First, the CIOU regression loss function in YOLOv8s is replaced with the WIoUv2 regression loss function, effectively alleviating the negative impact of uneven distribution of positive and negative samples during model training through a more balanced gradient gain distribution strategy, which significantly improves detection accuracy. Second, the CSPPC module replaces the original C2f module in the neck network, reducing the number of parameters and stabilizing model accuracy while lowering computational complexity and resource consumption. Finally, a new head layer P2-SLD was designed, which replaced the original detection head with a shared lightweight detection head SLD, introduced the P2 layer and removed the P5 layer, which improved the lightweight level of the model, improved the processing efficiency and reduced the energy consumption while ensuring the detection accuracy. Experimental results indicate that, compared to the benchmark model YOLOv8s, PdYOLO achieves significant improvements across several key performance indicators: Precision increased by 3.7%, Recall by 10.8%, and mAP@50 by 4.7%. Furthermore, the number of parameters in the model was reduced by 43.3%, from 11.13 million to 6.3 million, fully meeting

关键词： Accuracy Feature extraction Computational modeling YOLO Neck Classification algorithms Head Green products Convolution clustering algorithms Complex backgrounds deep learning fruit tree environment lightweight peach detection YOLOv8s

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A Comprehensive Survey on Community Detection With Deep Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第4期35卷 4682-4702页

作者： Su, Xing Xue, Shan Liu, Fanzhen Wu, Jia Yang, Jian Zhou, Chuan Hu, Wenbin Paris, Cecile Nepal, Surya Jin, Di Sheng, Quan Z. Yu, Philip S. Macquarie Univ Sch Comp Sydney NSW 2109 Australia CSIRO Data61 Sydney NSW 2015 Australia Chinese Acad Sci Acad Math & Syst Sci Beijing 100093 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years--particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

关键词： Deep learning Taxonomy Optimization Partitioning algorithms clustering algorithms Social networking (online) Peer-to-peer computing Community detection deep learning graph neural network network representation social networks

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A Software-Defined MARL-Based Architecture for AUV Cluster Network to Enable Cooperative and Smart Underwater Target Tracking

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IEEE WIRELESS COMMUNICATIONS 2024年第6期31卷 56-62页

作者： Zhu, Shengchao Han, Guangjie Lin, Chuan Hohai Univ Internet Things Engn Changzhou Peoples R China Hohai Univ Dept Internet Things Engn Changzhou Peoples R China Northeastern Univ Software Coll Shenyang Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing Peoples R China

With the rapid development of underwater network and communication technology (e.g., the underwater acoustic, optical communication technologies), Software-Defined Networking (SDN) has been widely promoted for underwater monitoring network-enabled applications, especially the emerging AUV cluster network-based underwater target tracking. However, to achieve high-performance and smart underwater target tracking, a significant challenge to design an intelligent architecture integrating efficient management and smart determination exists. In this article, based on the above motivation and our previous work at the SDN-enabled underwater monitoring networks, we propose a Software-Defined Multi-Agent Reinforcement Learning (SD-MARL) architecture for the AUV cluster network to combine the similarity between the SDN technology (centralized management, distributed operation) and the CTDE-based MARL architecture (centralized training, decentralized execution). Particularly, for the application with the SD-MARL, we propose an SD-MARL-based underwater target tracking scheme, integrating a cooperative control model, an SD-MARL-based beacon framework, a cooperative target tracking framework, and an interrupted obstacle avoidance framework, respectively. Evaluation results show that our proposed SD-MARL-based underwater target tracking scheme can perform efficient underwater target tracking and better than some traditional MARL architecture in tracking accuracy and underwater communication efficiency.

关键词： Training Task analysis Target tracking Computer architecture clustering algorithms Computational modeling Heuristic algorithms

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Modeling Topic-Specific Influential Users in QA Forums Using Association Rule Mining

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IEEE ACCESS 2024年 12卷 196498-196516页

作者： Ishfaq, Umar Ali, Tassawar Daud, Ali Alreshoodi, Mohammed Irshad, Azeem COMSATS Univ Islamabad Dept Comp Sci Wah Campus Wah Cantt 470040 Pakistan Rabdan Acad Fac Resilience Abu Dhabi U Arab Emirates Qassim Univ Appl Coll Unit Sci Res Buraydah 52571 Saudi Arabia GGC Asghar Mall Punjab Higher Educ Dept Fac Comp Sci Rawalpindi 46040 Pakistan

Social networks have enabled the exchange of information that has accelerated the diffusion of online content. Social networks offer multiple forms of social interaction that facilitate users in generating online content. The increasing popularity of social networks has attracted many researchers to investigate their impact on users and confirmed significant influence of social networks on markets, social life, and politics. Therefore, identification of the influential users in social networks has become a popular research area due to several of its applications in diverse domains such as e-commerce, viral marketing, political campaigns etc. However, earlier research either consider linguistic content or utilize network-based representation for finding the influential users in social networks. This paper incorporates association rule mining (ARM) based algorithms, that are mostly used for market-basket analysis, for exploring the behavior of users and predict their participation in social interactions. The study identifies topic-based influential users using Apriori and FPGrowth on the dataset of a popular online question-answer community. In addition, the study employs standard evaluation metrics such as confidence, support, lift, and conviction for computing association rules. Subsequently, the obtained results are validated using conventional measures including accuracy, precision, and recall in accordance with the perspective of association rules. The experiments are performed on multiple social networking datasets and the obtained results prove the visibility and quality of the proposed method against well-established degree centrality, PageRank, and HITS. In addition, the results validate the effectiveness of ARM in identifying influential users in social networks. The generated rule set can help create efficient decision support systems that are becoming prevalent in providing solutions to real-world problems.

关键词： Social networking (online) Association rule learning Message systems Market research Prediction algorithms Patents clustering algorithms Itemsets Text mining Technological innovation classification influential users QA forum social network analysis StackOverflow deep learning

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Towards Effective Clustered Federated Learning: A Peer-to-Peer Framework With Adaptive Neighbor Matching

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IEEE TRANSACTIONS ON BIG DATA 2024年第6期10卷 812-826页

作者： Li, Zexi Lu, Jiaxun Luo, Shuang Zhu, Didi Shao, Yunfeng Li, Yinchuan Zhang, Zhimeng Wang, Yongheng Wu, Chao Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Huawei Noahs Ark Lab Beijing 100085 Peoples R China Zhejiang Univ Sch Publ Affairs Hangzhou 310027 Peoples R China Zhejiang Univ Sch Software Technol Hangzhou 310027 Peoples R China Zhejiang Lab Big Data Intelligence Res Ctr Hangzhou 311121 Peoples R China

In federated learning (FL), clients may have diverse objectives, and merging all clients' knowledge into one global model will cause negative transfer to local performance. Thus, clustered FL is proposed to group similar clients into clusters and maintain several global models. In the literature, centralized clustered FL algorithms require the assumption of the number of clusters and hence are not effective enough to explore the latent relationships among clients. In this article, without assuming the number of clusters, we propose a peer-to-peer (P2P) FL algorithm named PANM. In PANM, clients communicate with peers to adaptively form an effective clustered topology. Specifically, we present two novel metrics for measuring client similarity and a two-stage neighbor matching algorithm based Monte Carlo method and Expectation Maximization under the Gaussian Mixture Model assumption. We have conducted theoretical analyses of PANM on the probability of neighbor estimation and the error gap to the clustered optimum. We have also implemented extensive experiments under both synthetic and real-world clustered heterogeneity. Theoretical analysis and empirical experiments show that the proposed algorithm is superior to the P2P FL counterparts, and it achieves better performance than the centralized cluster FL method. PANM is effective even under extremely low communication budgets.

关键词： clustering algorithms Peer-to-peer computing Federated learning Topology Servers Loss measurement Optimization Clustered federated learning distributed learning peer-to-peer communication

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