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A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

Computer Modeling in engineering & Sciences 2024年第9期140卷 2165-2206页

作者： Qusay M.Salih Md.Arafatur Rahman Ahmad Firdaus Mohammed Rajih Jassim Hasan Kahtan Jasni Mohamad Zain Ahmed Hussein Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Medical Equipment Technology Engineering Al Salam University CollegeBaghdadIraq School of Mathematics and Computer Science University of WolverhamptonWolverhampton01902UK Department of Production Engineering and Metallurgy University of TechnologyBaghdadIraq Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia Department Computer Science Al-Salam University CollegeBaghdadIraq

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already *** realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ***,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G ***,this study aims to review,examine,and summarize existing studies and research activities in *** study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and ***,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of *** study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

关键词： 6G bibliometric wireless networks review

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Deepfakes: A Review on Technologies, Applications and Strategies

Deepfakes: A Review on Technologies, Applications and Strate...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kaswan, Kuldeep Singh Malik, Kiran Dhatterwal, Jagjit Singh Naruka, Mahaveer Singh Govardhan, D. School of Computing Science and Engineering Galgotias University UP Greater Noida India Mriem Department of Computer Science and Engineering Rohtak India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh India G L Bajaj Institute of Technology and Management Greater Noida India Institute of Aeronautical Engineering Department of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798350357769

Deepfakes are artificial intelligence-generated multimedia, primarily videos or audio, that effectively edit or invent information by superimposing one person's likeness or voice onto that of another. They use deep learning techniques such as GAN s to generate hyper-realistic simulations that blur the line between reality and fiction. Deepfakes encompass everything from political satire to possible concerns in the fields of disinformation, privacy invasion, and cybersecurity. Understanding deepfakes is critical in an age when manipulated media may influence public opinion and undermine confidence. The primary objective of this paper is to provide a holistic overview of deepfake technology, shedding light on both its creative potential and inherent risks. Through a systematic exploration of the evolution, applications, techniques, and detection strategies, we aim to equip readers with an informed perspective on this ever-evolving field. By synthesizing existing knowledge and insights, this paper intends to contribute to the ongoing discourse surrounding deepfakes, enabling stakeholders to make informed decisions, develop effective countermeasures, and anticipate the future trajectory of this technology. Ultimately, our work strives to foster awareness and preparedness in an age where discerning the truth has never been more vital. © 2023 IEEE.

关键词： AI CNN Deepfakes GAN Machine Learning RNN

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Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations

arXiv

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arXiv 2024年

作者： Zhou, Ao Liu, Bin Wang, Jin Tsoumakas, Grigorios Key Laboratory of Data Engineering and Visual Computing School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and multi-class classification tasks, it has been demonstrated that batch selection algorithms preferring samples with higher uncertainty achieve better performance than difficulty-based methods. Although there are two batch selection methods tailored for multi-label data, none of them leverage important uncertainty information. Adapting the concept of uncertainty to multi-label data is not a trivial task, since there are two issues that should be tackled. First, traditional variance or entropy-based uncertainty measures ignore fluctuations of predictions within sliding windows and the importance of the current model state. Second, existing multi-label methods do not explicitly exploit the label correlations, particularly the uncertainty-based label correlations that evolve during the training process. In this paper, we propose an uncertainty-based multi-label batch selection algorithm. It assesses uncertainty for each label by considering differences between successive predictions and the confidence of current outputs, and further leverages dynamic uncertainty-based label correlations to emphasize instances whose uncertainty is synergistically expressed across multiple labels. Empirical studies demonstrate the effectiveness of our method in improving the performance and accelerating the convergence of various multi-label deep learning models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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Heterogeneous Face Recognition Algorithm: Convolution Neural Network Approach 5

Heterogeneous Face Recognition Algorithm: Convolution Neural...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sujatha, E. Manickam, M. Ani Minisha, R. Rekha, K.S. Indumathy, T. Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rr and Dr Sr Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India Department of Medical Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350385649

Heterogeneous face recognition, also known as HFR, is an approach to facial recognition that attempts to match facial pictures that have been gathered through a variety of sensory modalities. HFR is primarily used for forensics, network security, and all commercial applications. The heterogeneous face recognition (HFR) algorithm is robust in handling challenges that exist in conventional face recognition. This is since heterogeneous face images display an enormous amount of intra- class variation, and it does not support hybrid modality of face images. This study provides a unique technique to transfer learning as a means of mastering invariant facial feature characteristics in visual (VIS) face pictures. A wrapping procedure combines end to end connections of two components for deep network. A discriminator has the details of fine-grained data for improving the quality of image which depends on wavelets. The system is made up of two different modules: one deals with face recognition, while the other determines a person's gender. It analysis about the characteristics of gender from the image using Convolutional Neural Networks that have been pre-trained. To train CNN in the face recognition module, in particular, the use of the open-source datasets Labelled Faces within the Wild, YouTube Face, and VGGFace2 results in an improvement in accuracy. Deep learning techniques and a substantial number of photographic examples of people's faces are what make it feasible for the proposed recognition to function so admirably. The problem of studying recognition remains challenging due to the limited size of the sample and the information gap between the senses. © 2024 IEEE.

关键词： Face recognition

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On Pipelined GCN with Communication-Efficient Sampling and Inclusion-Aware Caching

On Pipelined GCN with Communication-Efficient Sampling and I...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Shulin Wang Qiang Yu Xiong Wang Yuqing Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Graph convolutional network (GCN) has achieved enormous success in learning structural information from unstructured data. As graphs become increasingly large, distributed training for GCNs is severely prolonged by frequent cross-worker communications. Existing efforts to improve the training efficiency often come at the expense of GCN performance, while the communication overhead persists. In this paper, we propose PSC-GCN, a holistic pipelined framework for distributed GCN training with communication-efficient sampling and inclusion-aware caching, to address the communication bottleneck while ensuring satisfactory model performance. Specifically, we devise an asynchronous pre-fetching scheme to retrieve stale statistics (features, embedding, gradient) of boundary nodes in advance, such that the embedding aggregation and model update are pipelined with statistics transmission. To alleviate communication volume and staleness effect, we introduce a variance-reduction based sampling policy, which prioritizes inner nodes over boundary ones for reducing the access frequency to remote neighbors, thus mitigating cross-worker statistics exchange. Complementing graph sampling, a feature caching module is co-designed to buffer hot nodes with high inclusion probability, ensuring that frequently sampled nodes will be available in local memory. Extensive evaluations on real-world datasets show the superiority of PSC-GCN over state-of-the-art methods, where we can reduce training time by 72%-80% without sacrificing model accuracy.

关键词： Training Estimation error Accuracy Graph convolutional networks Computational modeling Probability Benchmark testing

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Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1452-1463页

作者： Mei Liu Xiaoyan Zhang Mingsheng Shang Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810016China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Shenzhen Research Institute Lanzhou UniversityShenzhen 518063China

Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)*** obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA ***,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ***,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.

关键词： Consensus filter k-winners-take-all(kWTA) multirobot coordination noise resistance

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OFDMA Based UAVs Communication for Ensuring QoS

OFDMA Based UAVs Communication for Ensuring QoS

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Karabulut, Muhammet Ali Shahen Shah, A.F.M. Islam, Md Baharul Rana, Muhammad Ehsan Department of Electrical and Electronics Engineering Kafkas University Kars Turkey Department of Electronics and Communication Engineering Yildiz Technical University Istanbul Turkey School of Data Science and Engineering American University of Malta Bormla Malta School of Computing Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia

ISBN: (纸本)9789811948305

Unmanned aerial vehicles (UAV), more often familiar as drone, has been widely used both in civilian activities and military missions. UAV ad-hoc networks, also known as flying ad-hoc networks, are ad-hoc multi-UAV systems (FANETs). The FANET’s medium access control (MAC) and physical (PHY) layer specifications are defined by the IEEE 802.11 standard, which uses the enhanced distributed channel access function (EDCAF) to assure the quality of service (QoS) in the MAC layer. Because it suffers from collisions, carrier sensing multiple access with collision avoidance (CSMA/CA) is useless when a high data rate is required. As a result, the use of orthogonal frequency division multiple access (OFDMA) is advised. By lowering the collision probability, OFDMA increases performance and reduces latency. This study develops an analytical model for assessing IEEE 802.11 EDCAF performance for OFDMA-based UAV communication. A new channel contention method that combines EDCAF and OFDMA is suggested. Firstly, EDCA based prioritized channel contention is done. Then, channel access and transmission are done using OFDMA. We undertake a theoretical study based on the Markov chain model, which gives throughput, packet drop rate (PDR), and delay expressions. The simulation results verify the model performance and show that the proposed technique ensures strict delay requirements and supports QoS. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Frequency division multiple access

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Communication-Control Codesign for Large-Scale Wireless Networked Control Systems

arXiv

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arXiv 2024年

作者： Pang, Gaoyang Liu, Wanchun Niyato, Dusit Vucetic, Branka Li, Yonghui The School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Wireless Networked Control Systems (WNCSs) are essential to Industry 4.0, enabling flexible control in applications, such as drone swarms and autonomous robots. The interdependence between communication and control requires integrated design, but traditional methods treat them separately, leading to inefficiencies. Current codesign approaches often rely on simplified models, focusing on single-loop or independent multi-loop systems. However, large-scale WNCSs face unique challenges, including coupled control loops, time-correlated wireless channels, trade-offs between sensing and control transmissions, and significant computational complexity. To address these challenges, we propose a practical WNCS model that captures correlated dynamics among multiple control loops with spatially distributed sensors and actuators sharing limited wireless resources over multi-state Markov block-fading channels. We formulate the codesign problem as a sequential decision-making task that jointly optimizes scheduling and control inputs across estimation, control, and communication domains. To solve this problem, we develop a Deep Reinforcement Learning (DRL) algorithm that efficiently handles the hybrid action space, captures communication-control correlations, and ensures robust training despite sparse cross-domain variables and floating control inputs. Extensive simulations show that the proposed DRL approach outperforms benchmarks and solves the large-scale WNCS codesign problem, providing a scalable solution for industrial automation. © 2024, CC BY.

关键词： Fading channels

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Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks

arXiv

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arXiv 2024年

作者： Wang, Zhiying Sun, Gang Wang, Yuhui Yu, Hongfang Niyato, Dusit School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computing and Data Science Nanyang Technological University Singapore

The Space-Air-Ground Integrated Network (SAGIN) framework is a crucial foundation for future networks, where satellites and aerial nodes assist in computational task offloading. The low-altitude economy, leveraging the flexibility and multifunctionality of Unmanned Aerial Vehicles (UAVs) in SAGIN, holds significant potential for development in areas such as communication and sensing. However, effective coordination is needed to streamline information exchange and enable efficient system resource allocation. In this paper, we propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN. The CMADDPG algorithm leverages dynamic UAV clustering to partition UAVs into clusters, each managed by a Cluster Head (CH) UAV, facilitating a distributed-centralized control approach. Within each cluster, UAVs delegate offloading decisions to the CH UAV, reducing intra-cluster communication costs and decision conflicts, thereby enhancing task scheduling efficiency. Additionally, by employing a multi-agent reinforcement learning framework, the algorithm leverages the extensive coverage of satellites to achieve centralized training and distributed execution of multi-agent tasks, while maximizing overall system profit through optimized task offloading decision-making. Simulation results reveal that the CMADDPG algorithm effectively optimizes resource allocation, minimizes queue delays, maintains balanced load distribution, and surpasses existing methods by achieving at least a 25% improvement in system profit, showcasing its robustness and adaptability across diverse scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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