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IEEE Conference on Global communications (GLOBECOM)

作者： Wen-Yu Dong Shaoshi Yang Wei Lin Wei Zhao Jia-Xing Gui Sheng Chen School of Information and Communication Engineering Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education State Key Laboratory of Networking and Switching Technology Beijing China School of Electronics and Computer Science University of Southampton Southampton U.K

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In harsh environments such as mountainous terrain, dense vegetation areas, or urban landscapes, a single type of unmanned aerial vehicles (UAVs) may encounter challenges like flight restrictions, difficulty in task execution, or increased risk. Therefore, employing multiple types of UAVs, along with satellite assistance, to collaborate becomes essential in such scenarios. In this context, we present a stochastic geometry based approach for modeling the heterogeneous non-terrestrial networks (NTNs) by using the classical nbinomial point process and introducing a novel point process, called Matérn hard-core cluster process (MHCCP). Our MHCCP possesses both the exclusivity and the clustering properties, thus it can better model the aircraft group composed of multiple clusters. Then, we derive closed-form expressions of the outage probability (OP) for the uplink (aerial-to-satellite) of heterogeneous NTNs. Unlike existing studies, our analysis relies on a more advanced system configuration, where the integration of beamforming and frequency division multiple access, and the shadowed-Rician (SR) fading model for interference power, are considered. The accuracy of our theoretical derivation is confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization of NTNs.

关键词： Geometry Analytical models Closed-form solutions Atmospheric modeling Stochastic processes Interference Probability Power system reliability Uplink Aircraft

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SLA-driven container consolidation with usage prediction for green cloud computing

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Frontiers of computer Science 2020年第1期14卷 42-52页

作者： Jialei LIU Shangguang WANG Ao ZHOU Jinliang XU Fangchun YANG State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100029China School of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhou 450002China

Since service level agreement(SLA)is essentially used to maintain reliable quality of service between cloud providers and clients in cloud environment,there has been a growing effort in reducing power consumption while complying with the SLA by maximizing physical machine(PM)-level utilization and load balancing techniques in infrastructure as a ***,with the recent introduction of container as a service by cloud providers,containers are increasingly popular and will become the major deployment model in the cloud environment and specifically in platform as a ***,reducing power consumption while complying with the SLA at virtual machine(VM)-level becomes *** this context,we exploit a container consolidation scheme with usage prediction to achieve the above *** obtain a reliable characterization of overutilized and underutilized PMs,our scheme jointly exploits the current and predicted CPU utilization based on local history of the considered PMs in the process of the container *** demonstrate our solution through simulations on real *** experimental results show that the container consolidation scheme with usage prediction reduces the power consumption,number of container migrations,and average number of active VMs while complying with the SLA.

关键词： container consolidation service level agreement power consumption usage prediction

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Hybrid Human-Artificial Intelligence Enabled Edge Caching Based on Interest Evolution 14th

Hybrid Human-Artificial Intelligence Enabled Edge Caching Ba...

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14th EAI International Conference on Mobile Multimedia communications, Mobimedia 2021

作者： Li, Zhidu Li, Fuxiang Wu, Dapeng Wang, Honggang Wang, Ruyan School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Optical Communication and Networks Chongqing China Key Laboratory of Ubiquitous Sensing and Networking Chongqing China Electrical and Computer Engineering Department University of Massachusetts Dartmouth Dartmouth United States

ISBN: (纸本)9783030898137

How to cache appropriable contents for users from huge amount of candidates is a challenge in edge caching network. To address this challenge, this paper studies an edge caching scheme based on user interest, where an interest extraction and evolution network is developed. Specifically, the input features are first classified and embedding. The user interest is then mined and modeled according to the user historical behaviors with the gated recurrent unit network. Thereafter, the user interest evolution process is studied by analyzing the impact of the previous interests on the current interest through an attention mechanism. The group interest model is further studied by merging user interest evolution and social relationships among contents, based on which edge caching scheme is obtained. The effectiveness of the proposed scheme is finally validated by extensive experiments with a real-world dataset. The analysis in this paper sheds new light on edge content caching from user interest evolution perspective. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Behavioral research

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Collaborative Block Mining and Edge Task Offloading in MEC-Assisted Blockchain Networks: A Coalition Game-Theoretic Approach

Collaborative Block Mining and Edge Task Offloading in MEC-A...

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IEEE Conference on Global communications (GLOBECOM)

作者： Licheng Ye Jingjing Luo Changkun Jiang Lin Gao School of Electronics and Information Engineering Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Shenzhen China Department of Computer Engineering Shenzhen University Shenzhen China

Mobile edge computing (MEC) is a promising technology for improving the efficiency and security of mobile blockchain networks, by allowing miners with limited computing resources to offload the computation-intensive mining tasks to edge computing servers that are proximate to them. Collaborative block mining can further improve the mining efficiency and increase the miner profit, by enabling multiple miners to pool their computation resources and transaction data together to mine new blocks collaboratively. Thus, an MEC-assisted collaborative blockchain network can leverage the advantages of both technologies, offering superior efficiency, security, and scalability for blockchains. While existing research in this area mainly focused on the single-coalition collaboration mode where each miner can only join one collaborative coalition, this work explores a more comprehensive multi-coalition collaboration mode, which allows each miner to join multiple collaborative coalitions. To analyze the miner behavior in such a scenario, we formulate a novel two-layer sequential game, consisting of a coalition formation game as the first-layer and an edge resource competition game (among the formed coalitions) as the second layer. Specifically, in the first layer, each miner acts as a game player and selects multiple coalitions to join, leading to an overlapping coalition formation (OCF) game among miners. In the second layer, each established coalition acts as a game player and decides the amount of edge computing resource to invest, leading to an edge resource competition (ERC) game among coalitions. We derive the closed-form Nash equilibrium for the ERC game, and propose an iterative algorithm that converges to a stable coalition structure for the OCF game. Simulation results show that the proposed multi-coalition collaboration mode can improve the system efficiency by 34.1% ~ 54.3%, compared to the single-coalition collaboration mode.

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Precise Timing over Beyond 5G Networks for Intelligent Transport in the Smart City

Precise Timing over Beyond 5G Networks for Intelligent Trans...

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IEEE International Symposium on Personal, Indoor and Mobile Radio communications (PIMRC)

作者： Hongxing Li Guochu Shou Yaqiong Liu Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The need for precise timing is increasing with the development of the smart city, and intelligent transport is a key application that often relies on the distribution of accurate timing. Existing time synchronization solutions cannot adequately meet the precise timing needs for practical applications in intelligent transportation. Global Navigation Satellite Systems (GNSS) signals, for instance, can be obstructed, weakened, or deflected in urban canyon environments. While certain sectors like telecommunications and smart grids have successfully adopted IEEE 1588 for high-precision time synchronization, its dependence on standalone networks and limited application within local area networks poses challenges when catering to the wide-ranging and mobile demands of intelligent transportation applications. This paper categorizes the time synchronization requirements of intelligent transport and proposes a time synchronization scheme over converged networks, which separates time transmission and precision compensation based on software-defined networking (SDN) principles, and makes it possible to provide precise timing for intelligent transport devices, including on-board units (OBUs) and roadside units (RSUs), over wired and wireless communication networks. Experimental results indicate that the proposed scheme achieves a relative time error of less than 1.2 microseconds over the converged networks.

关键词： Wireless communication Global navigation satellite system Accuracy Smart cities Transportation Telecommunications Smart grids Synchronization Software defined networking Local area networks

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QoS-Aware Joint Massive Random Access Control and Resource Allocation With Colliding Preamble Reuse for Prioritized IoT

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IEEE Transactions on Vehicular Technology 2025年

作者： Guo, Ziming Zhu, Xu Wei, Zhongxiang Cao, Jie Jiang, Yufei Lau, Vincent K.N. Sun, Sumei Harbin Institute of Technology School of Information Science and Technology Shenzhen518055 China Shenzhen Municipal Key Laboratory of AIoT Communication Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China TongJi University College of Electronic and Information Engineering Shanghai Institute of Intelligent Science and Technology China Southeast University National Mobile Communications Research Laboratory Nanjing China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering 999077 Hong Kong Institute for Infocomm Research Agency for Science 138632 Singapore

Enabling the current Internet-of-Things (IoT) and the future Internet-of-Everything (IoE) paradigms in the existing networks is challenging due to the low access efficiency amidst massive random access (RA) requests and heterogeneous applications. To address this, we investigate a novel prioritized RA control technique for massive IoT networks that cooperatively identifies adaptive access class barring (ACB) factors and preamble allocations, taking into account IoT devices' heterogeneity and limited RA resources. We derived the closed-form expression for the preamble utilization ratio (PAUR), incorporating the reusable colliding preambles and the early-stage collision detection (es-CD) technique. Two priority-guided RA request transition models are established, precisely translating RA priorities into a flexible backoff window size. Our Markov-Chain-based analytical model enables a comprehensive RA performance evaluation, demonstrating the superior performance of the proposed QoS-Aware Joint Access Control and Resource Allocation (QA-JACRA) in terms of PAUR, access delay, and throughput. This is achieved at a lower computational complexity compared to existing RA control schemes, providing a promising design approach for the activation of massive IoT devices. © 2025 IEEE.

关键词： Markov chains

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A Novel Blockchain-Based Revocable Permission Management on CP-ABE Scheme 7th

A Novel Blockchain-Based Revocable Permission Management on ...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Xv, Qianchuan Song, Yubo Li, Xuan Hu, Aiqun Wang, Junbo Key Laboratory of Computer Networking Technology of Jiangsu Province Nanjing211189 China Purple Mountain Laboratory of Network Communication and Security Nanjing211189 China School of Information Science and Engineering Nanjing211189 China National Mobile Communications Research Laboratory Nanjing211189 China

ISBN: (纸本)9783030786205

Relying on the characteristics of decentralized, tamper-proof, and traceable, blockchain technology has begun to be widely used in permission management. It can be used for access control by using smart contracts to distribute tokens. But also because of the immutability of the blockchain, it cannot dynamically revoke permission. This paper proposed an on-chain user permission management mechanism that could support automatic and active permission revocation. This model proposed an extended CP-ABE chain user permission management model, which realized the permission grant based on user attributes by smart contracts. Furthermore, on this basis, an extended CP-ABE permission revocation mechanism was proposed. The timestamp technology was used to realize the automatic revocation of permission, and the subset coverage algorithm was used to realize the active revocation of permission. A blockchain-based permission distribution model was built, and the permission revocation scheme was introduced into it to conduct experiments. Its performance was tested and analyzed. The experimental results showed that when the number of permission distributions is 1000, the delay is about 5 s. At the same time, the permission change is above 130TPS, which met actual application requirements. © 2021, Springer Nature Switzerland AG.

关键词： Blockchain

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High-Performance Low-Complexity Hierarchical Frequency Synchronization for Distributed Massive MIMO-OFDMA Systems

arXiv

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arXiv 2023年

作者： Wang, Xiao-Yang Yang, Shaoshi Yuan, Tian-Hao Zhai, Hou-Yu Zhang, Jianhua Hanzo, Lajos School of Information and Communication Engineering Beijing University of Posts and Telecommunications The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing100876 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications The State Key Laboratory of Networking and Switching Technology Beijing100876 China School of Electronics and Computer Science University of Southampton United Kingdom

We propose a high-performance yet low-complexity hierarchical frequency synchronization scheme for orthogonal frequency-division multiple-access (OFDMA) aided distributed massive multi-input multi-output (MIMO) systems, where multiple carrier frequency offsets (CFOs) have to be estimated in the uplink. To solve this multi-CFO estimation problem efficiently, we classify the active antenna units (AAUs) as the master and the slaves. Then, we split the scheme into two stages. During the first stage the distributed slave AAUs are synchronized with the master AAU, while the user equipment (UE) is synchronized with the closest slave AAU during the second stage. The mean square error (MSE) performance of our scheme is better than that of the representative state-of-the-art baseline schemes, while its computational complexity is substantially lower. Copyright © 2023, The Authors. All rights reserved.

关键词： Frequency division multiple access

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Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks

arXiv

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arXiv 2024年

作者： Li, Zuguang Wu, Shaohua Li, Liang Zhang, Songge School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen518055 China Frontier Research Center Pengcheng Laboratory Shenzhen518055 China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Shenzhen518055 China School of Electronic and Computer Engineering Peking University Shenzhen518000 China

In this letter, we propose an energy-efficient split learning (SL) framework for fine-tuning large language models (LLMs) using geo-distributed personal data at the network edge, where LLMs are split and alternately across massive mobile devices and an edge server. Considering the device heterogeneity and channel dynamics in edge networks, a Cut lAyer and computing Resource Decision (CARD) algorithm is developed to minimize training delay and energy consumption. Simulation results demonstrate that the proposed approach reduces the average training delay and server’s energy consumption by 70.8% and 53.1%, compared to the benchmarks, respectively. Copyright © 2024, The Authors. All rights reserved.

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One-shot Learning for Channel Estimation in Massive MIMO Systems

One-shot Learning for Channel Estimation in Massive MIMO Sys...

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IEEE Conference on Vehicular Technology (VTC)

作者： Kai Kang Qiyu Hu Yunlong Cai Yonina C. Eldar College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) Hangzhou China Department of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

In conventional supervised deep learning based channel estimation algorithms, a large number of training samples are required for offline training. However, in practical communication systems, it is difficult to obtain channel samples for every signal-to-noise ratio (SNR). Furthermore, the generalization ability of these deep neural networks (DNN) is typically poor. In this work, we propose a one-shot self-supervised learning framework for channel estimation in multi-input multi-output (MIMO) systems. The required number of samples for offline training is small and our approach can be directly deployed to adapt to variable channels. Our framework consists of a traditional channel estimation module and a denoising module. The denoising module is designed based on the one-shot learning method Self2Self and employs Bernoulli sampling to generate training labels. Besides,we further utilize a blind spot strategy and dropout technique to avoid overfitting. Simulation results show that the performance of the proposed one-shot self-supervised learning method is very close to the supervised learning approach while obtaining improved generalization ability for different channel environments.

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