检索结果-内蒙古大学图书馆

TechRxiv 2022年

作者： Zhang, Long Ma, Xiaozheng Zhuang, Zirui Xu, Haitao Sharma, Vishal Han, Zhu The School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Electronics Electrical Engineering and Computer Science Queen’s University BelfastBT9 5BN United Kingdom The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

This paper studies the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which employs the cognitive radio into a swarm of UAVs within a three-layer hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a Q-learning framework to achieve the intelligent routing to maximize the utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric jointly captures the achievable rate and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for UAV to optimize the candidate UAV selection. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update Q-values during the training process for achieving a fast convergence speed. Simulation results are provided to assess the potential of the Q-learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm converges in a few number of iterations. Furthermore, the proposed algorithm can increase the accumulated rewards, and achieve significant performance gains, as compared to the benchmark schemes. © 2022, CC BY.

关键词： Cognitive radio

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Multi-Slot Distributed Measurement Selection: A Sparsity Learning Approach

Multi-Slot Distributed Measurement Selection: A Sparsity Lea...

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作者： Xia, Qian Wang, Wei Ran, Rong Gong, Yi Zhang, Zhaoyang College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China School of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China

When only a part of measurements are collected sequentially in large-scale Internet of Things (IoT) networks, it is motivated to extract significant information from the collected data for utilizing the communication resources over incoming slots more efficiently. In this article, we study the successive measurement selection problem for sparse parameter estimation and its distributed implementation in large-scale networks. Specifically, we transform the intractable multi-slot measurement selection problem into per-slot subproblems, and in each slot we select the uncollected measurements that are more 'useful' for parameter estimation based on sparse Bayesian learning. In order to deal with the combinatorial problem with distributed implementation, we relax the subproblem and propose an enhanced subgradient method to solve the Boolean constraint. The proposed distributed scheme needs only a common information broadcast by the centralized controller, where the communication overheads only depend on the number of sparse active sources rather than the number of sensors which is always huge. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes. When the total communication overheads are limited with the network scale, our scheme presents extreme practicability and applicability for the large-scale networks. © 2015 IEEE.

关键词： Wireless sensor networks

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Semantic communications:Theories,Technologies and Applications

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中国通信（英文版） 2024年第7期21卷前插1-前插5页

作者： Zhang Ping Shi Guangming Cui Shuguang Zhang Zhaoyang Niu Kai Xiao Yong Qin Zhijin Dai Jincheng Shao Shuo Deniz Gündüz Eleonora Grassucci School of Information and Communication Engineering at Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology IMT-2020(5G)Experts Panel Experts Panel for China's 6G development Peng Cheng Laboratory School of Artifi-cial Intelligence Xidian University electrical and computer engineering with the University of Arizona Texas A&M University UC Davis Chinese Univer-sity of Hong Kong(Shenzhen)(CUHK-Shenzhen) Zhejiang University School of Artificial Intelli-gence BUPT School of Electron-ic Information and Communications at the Huazhong University of Science and Technology(HUST) WuhanChina Peng Cheng Laboratory Guang-zhouChina and the Pazhou Laboratory(Huangpu)Guang-zhouChina Tsinghua University BeijingChina Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and Telecommunications School of Electronics Informa-tion and Electrical EngineeringShanghai Jiao Tong Univer-sityChina Information Processing in the Electrical and Electronic Engineering Department at Imperial College London UK Intelligent Systems and Networks Group Department at Imperial College London UK Department of Information Engineering Electronicsand Telecommunications of the Sapienza University of Rome

The last seventy years have witnessed the transi-tion of communication from Shannon's theoretical concept to current high-efficiency practical *** respect to Shannon information theory,to fulfill the high-bandwidth utilization,any further increase in the data rate requires a significant augmentation in the received signal power unless the bandwidth is extended in proportion to the incre-mental data rate.

关键词： Special issues and sections Semantics communication systems Data processing Information theory Data augmentation

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Multiattack intrusion detection algorithm for edge-assisted internet of things 2

Multiattack intrusion detection algorithm for edge-assisted ...

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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

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

ISBN: (数字)9781728129778

ISBN: (纸本)9781728129778

With the continuous development of edge-assisted internet of things (IoT) applications, a large number of computing tasks need to be offloaded into the edge servers. It is critical to secure the security of IoT data transmission to the edge. In this paper, we propose a multi-attack intrusion detection system (IDS) for edge-assisted IoT using the back propagation (BP) Neural Network, and Radial basis function (RBF) neural network. Especially, BP neural network is exploited to find the abnormal and get the importance of features for each attack. Radial basis function (RBF) neural network is used for detecting multi-attack intrusion. The results show high accuracy in proposed multi-attack scenario and elucidate the feasibility and preeminence of our proposed anomaly detection framework. © 2019 IEEE.

关键词： Intrusion detection

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Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

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computers, Materials & Continua 2021年第2期 1461-1477页

作者： Zaiwar Ali Sadia Khaf Ziaul Haq Abbas Ghulam Abbas Lei Jiao Amna Irshad Kyung Sup Kwak Muhammad Bilal Telecommunications and Networking(TeleCoN)Research Laboratory GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Faculty of Computer Sciences and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Department of Information and Communication Technology University of Agder(UiA)Grimstad4898Norway Department of Information and Communication Engineering Inha UniversityIncheon22212Korea Department of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesGyeonggi-do17035Korea

In mobile edge computing(MEC),one of the important challenges is how much resources of which mobile edge server(MES)should be allocated to which user equipment(UE).The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU *** paper presents a novel comprehensive utility function for resource allocation in *** utility function considers the heterogeneous nature of applications that a UE offloads to *** proposed utility function considers all important parameters,including CPU,RAM,hard disk space,required time,and distance,to calculate a more realistic utility value for ***,we improve upon some general algorithms,used for resource allocation in MEC and cloud computing,by considering our proposed utility *** name the improved versions of these resource allocation schemes as comprehensive resource allocation *** UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these *** utility function depends upon the UE requests and the distance between UEs and MES,and serves as a realistic means of comparison between different types of UE ***(or selecting)an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging *** show that MES resource allocation is sub-optimal if CPU is the only resource *** taking into account the other resources,i.e.,RAM,disk space,request time,and distance in the utility function,we demonstrate improvement in the resource allocation algorithms in terms of service rate,utility,and MES energy consumption.

关键词： Cloud computing energy efficient resource allocation mobile edge computing service rate user equipment utility function

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Encoding Broad Learning System : An Effective Shallow Model For Anti-fraud

Encoding Broad Learning System : An Effective Shallow Model ...

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IEEE International Conference on Big Data

作者： Rui Zhong Zhen Zhang Rongheng Lin Hua Zou State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Science and Technology on Communication Networks Laboratory National Computer Network Emergency Response Technical Team/Coordination Center of China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

The criminal behavior of telecom fraud is increasing rapidly with the development of the communication industry, causing huge losses every year. The commonly used traditional fraud detection methods are less flexible. Currently, a more accurate and timely method is needed to deal with the evolving fraud. However, with the development of deep learning, more and more complex structures and a large number of parameters lead to the more time-consuming training process and poor interpretability which are unacceptable in the field of anti-fraud. Therefore, this paper proposes a shallow model called Encoding Broad Learning by incorporating the denoising autoencoder into the Broad Learning System (BLS). We use the first 15 seconds of the call content to process the text data of the fraudulent call by constructing TF-IDF, adding Gaussian noise to it, and combining with the denoising autoencoder to learn more general and robust features in the data. Then it is transformed into a neural network based on BLS, and fraudulent calls are identified on this model. This method is extremely suitable for fraud identification systems with few data features and high real-time training requirements. At the same time, in order to further improve the training efficiency and solve the potential memory explosion problem, we propose an integrated learning algorithm for parallel training of EBLS. Experiments and detailed analysis of the above methods are carried out. The results show that compared with the existing classic network algorithms, this method has a faster training speed, can ensure the accuracy and timeliness of online fraud identification, and help quickly identify fraudulent calls. The paper visualized the training results of these algorithms after processing the relevant parameters of EBLS training. These algorithms have strong interpretability and better meet the high security requirements in the field of anti-fraud.

关键词： Training Learning systems Noise reduction Big Data Encoding Telecommunications Security

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Attribute Prototype Network for Any-Shot Learning

arXiv

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arXiv 2022年

作者： Xu, Wenjia Xian, Yongqin Wang, Jiuniu Schiele, Bernt Akata, Zeynep State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Computer Vision Laboratory ETH Zurich Switzerland University of Tübingen Tübingen Germany Max Planck Institute for Intelligent Systems Tübingen Germany City University of Hong Kong Hong Kong Hong Kong Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences China

Any-shot image classification allows to recognize novel classes with only a few or even zero samples. For the task of zero-shot learning, visual attributes have been shown to play an important role, while in the few-shot regime, the effect of attributes is underexplored. To better transfer attribute-based knowledge from seen to unseen classes, we argue that an image representation with integrated attribute localization ability would be beneficial for any-shot, i.e. zero-shot and few-shot, image classification tasks. To this end, we propose a novel representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. Furthermore, we introduce a zoom-in module that localizes and crops the informative regions to encourage the network to learn informative features explicitly. We show that our locality augmented image representations achieve a new state-of-the-art on challenging benchmarks, i.e. CUB, AWA2, and SUN. As an additional benefit, our model points to the visual evidence of the attributes in an image, confirming the improved attribute localization ability of our image representation. The attribute localization is evaluated quantitatively with ground truth part annotations, qualitatively with visualizations, and through welldesigned user studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Image representation

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Modeling and Performance Analysis of IoT-over-LEO Satellite Systems under Realistic Operational Constraints: A Stochastic Geometry Approach

arXiv

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arXiv 2025年

作者： Dong, Wen-Yu Yang, Shaoshi Zhang, Ping Chen, Sheng School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Universal Wireless Communications Ministry of Education Beijing100876 China Key Laboratory of Mathematics and Information Networks Ministry of Education Beijing100876 China Key Laboratory of Networking and Switching Technology Beijing100876 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

The growing demand for reliable and extensive connectivity has made low Earth orbit (LEO) satellites aided Internet of Things (IoT) systems a critical area of research. However, current theoretical studies on IoT-over-LEO satellite systems often rely on unrealistic assumptions, such as infinite terrestrial areas and omnidirectional satellite coverage, leaving significant gaps in theoretical analysis for more realistic operational constraints. These constraints involve finite terrestrial area, limited satellite coverage, Earth curvature effect, integral uplink and downlink analysis, and link-dependent interference. To address these gaps, this paper proposes a novel stochastic geometry based model to rigorously analyze the performance of IoT-over-LEO satellite systems. By adopting a binomial point process (BPP) instead of the conventional Poisson point process (PPP), our model accurately characterizes the geographical distribution of a fixed number of IoT devices in a finite terrestrial region. This modeling framework enables the derivation of distance distribution functions for both the links from the terrestrial IoT devices to the satellites (T-S) and from the satellites to the Earth station (S-ES), while also accounting for limited satellite coverage and Earth curvature effects. To realistically represent channel conditions, the Nakagami fading model is employed for the TS links to characterize diverse small-scale fading environments, while the shadowed-Rician fading model is used for the S-ES links to capture the combined effects of shadowing and dominant line-of-sight paths. Furthermore, the analysis incorporates uplink and downlink interference, ensuring a comprehensive evaluation of system performance. The accuracy and effectiveness of our theoretical framework are validated through extensive Monte Carlo simulations. These results provide insights into key performance metrics, such as coverage probability and average ergodic rate, for both individual links and

关键词： Distribution functions

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Modeling, Capacity Studies, Antenna and System Designs for 6G/B6G 3D Continuous-Space Radio Channels Enabled by Electromagnetic Information Theory

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IEEE communications Surveys and Tutorials 2025年

作者： Wang, Cheng-Xiang Li, Junling Huang, Jie Huang, Chen Zhang, Zhaoyang Liu, Ying Zhou, Shidong Chen, Yunfei You, Xiaohu Gao, Xiqi Cui, Tie Jun Renzo, Marco Di Thompson, John Haas, Harald Schober, Robert Zhang, Rui Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking College of Information Science and Electronic Engineering Hangzhou310007 China Xidian University National Key Laboratory of Radar Detection and Sensing Xi'an710071 China Tsinghua University State Key Laboratory on Microwave and Digital Communications Beijing100084 China University of Durham Department of Engineering DurhamDH1 3LE United Kingdom Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing211189 China UniversitèParis-Saclay Laboratoire des Signaux et Systèmes CNRS CentraleSupélec Gif-sur-Yvette91192 France Centre for Telecommunications Research - Department of Engineering King's College London LondonWC2R 2LS United Kingdom School of Engineering EdinburghEH9 3BF United Kingdom Cambridge University Department of Engineering CambridgeCB2 1PZ United Kingdom Institute for Digital Communications Erlangen91054 Germany The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

Channel theory is a fundamental theory of wireless communications. The sixth generation (6G) and beyond 6G (B6G) wireless communication networks are expected to provide space-air-ground-sea global coverage. Base stations and users tend to move in three dimensional (3D) continuous spaces, while antennas and propagation environments can be tightly coupled. The underlying channels show an evolutionary trend to 3D continuous-space radio channels that combine antennas and wireless propagation channels, in comparison to discrete local-space wireless propagation channels in previous generations. This introduces new challenges for channel modeling, channel capacity analysis, antenna design, system design, etc. To address these challenges, this paper performs a comprehensive study on 3D continuous-space radio channels in 6G/B6G with the aid of electromagnetic information theory (EIT) that integrates electromagnetic theory, information theory, wireless propagation channel modeling theory, and antenna theory. We start by revealing the connections and gaps between these four fundamental theories. Then, an in-depth investigation on the four major research thrusts of 3D continuous-space radio channels is provided: 1) channel measurements and modeling, 2) channel capacity analysis, 3) general antenna design, and 4) wireless system design. We aim to explore the intrinsic relationships between antenna parameters, channel parameters, channel characteristics, channel capacity, and communication system performance. Finally, future research directions and challenges for 3D continuous-space radio channels are outlined. Our study endeavors to establish a fundamental framework for 3D continuous-space radio channels, with the potential to catalyze breakthroughs in 6G/B6G theories. © 1998-2012 IEEE.

关键词： Antenna grounds

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GJRA: A global joint resource allocation scheme for UAV service of PEC in IIoTs

arXiv

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arXiv 2020年

作者： Wang, Jin Jin, Caiyan Tang, Qiang Xiong, Naixue School of Computer Science and Communication Engineering Changsha University of Science and Technology Changsha Hunan410114 China Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin University Tianjin300000 China

The Industrial Internet of Things (IIoT) is an emerging paradigm to make industrial operations more efficient and intelligent by deploying a massive number of wireless devices to industry scenes. However, due to the limited computing capability and batteries, the Industrial Internet of Things Devices (IIoTDs) can't perform the computation-intensive or delay-sensitive tasks well and provide long-term services in practical. To tackle these challenges, we present an effective global joint resource allocation scheme for Unmanned Aerial Vehicle (UAV) service of Pervasive Edge Computing (PEC) in Industrial Internet of Things (IIoTs) and studied a collaborative UAV server-IIoTDs scheme in this paper. In our proposed scheme, the IIoTDs can keep high-efficiency performance even their battery ran out, by deploying the UAV as a PEC server and a mobile power source to provide task offloading and energy harvesting opportunities for IIoTDs. In consideration of the offloading position selection, devices resource allocation and system performance, we aim to minimize the overall service latency of all IIoTDs consisting of task computation latency and offloading latency, by joint optimizing the task offloading decisions, charging resources allocation, connection management, and UAV computation resources allocation. However, the formulated optimization problem is a mixed-integer nonlinear programming (MINLP) problem which is challenging to solve in general. In order to address the problem, we decompose it into multiple convex sub-problems based on block-coordinate descent (BCD) method to obtain the optimal solution. Performance evaluation demonstrates that our scheme outperforms the existing schemes in terms of the overall service latency of IIoTDs. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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