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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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Deep Reinforcement Learning based Green Resource Allocation Mechanism in Edge Computing driven Power Internet of Things 16

Deep Reinforcement Learning based Green Resource Allocation ...

引用

16th IEEE International Wireless communications and Mobile Computing Conference, IWCMC 2020

作者： Yang, Mo Yu, Peng Wang, Ying Huang, Xiuli Miu, Weiwei Yu, Pengfei Li, Wei Yang, Ruxia Tao, Minxing Shi, Lei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Global Energy Interconnection Research Institute Co. Ltd Beijing China Communication Branch State Grid Jiangsu Electric Power Co. Ltd Jiangsu China School of Computer Science Beijing University of Posts and Telecommunications Beijing China Department of Computing Carlow Institute of Technology Carlow Ireland

ISBN: (纸本)9781728131290

Smart grid deploys a large number of smart terminals and sensing devices to form an edge network, as well as a virtual network of information space and the power Internet of Things. As a key component of 5G and future network, the latency of end-to-end and the traffic of backhaul link could be reduced by edge network. Nevertheless, the function of storage and computing are moved down to the edge nodes in mobile edge network which increases the complexity of resource management. So it is an important issue to find out a more effectively resources allocation mechanism as well as meeting the requirements of each user. Edge computing refers to the processing of large amounts of edge data in the edge space in the edge network, thereby reducing dependence on the data center, achieving limited self-governance of the edge network, and reducing off-line threats. Although Deep Reinforcement Learning (DRL) has been applied to many of the work related to edge networks, there lacks the applications for green resource allocation. A Deep Reinforcement Learning (DRL) based green resource allocation mechanism is proposed in this paper which aims at efficiently allocating the resources while satisfying the needs of mobile users. The value of energy efficiency can be obtained when the algorithm achieves convergence according to the simulation results. The efficiency of the DRL-based mechanism and its effectiveness in meeting user requirements and implementing green resource allocation are validated. © 2020 IEEE.

关键词： Internet of things

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A geometry-based stochastic model for truck communication channels in freeway scenarios

arXiv

引用

arXiv 2021年

作者： Huang, Chen Wang, Rui Wang, Cheng-Xiang Tang, Pan Molisch, Andreas F. Pervasive Communication Research Center Purple Mountain Laboratories Nanjing211111 China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Ming Hsieh Department of Electrical and Computer Engineering USC Los AngelesCA90089 United States

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels;only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then cluster the MPCs based on their evolution patterns. We classify the determined clusters as line-of-sight, multiple-bounce reflections from static interaction objects (IOs), multiple-bounce reflections from mobile IOs, multiple-bounce reflections, and diffuse scattering. Specifically, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This article presents the complete parameterization of the channel model. We validate this model by contrasting the root-mean-square delay spread and the angular spreads of departure/arrival derived from the channel model with the outcomes directly derived from the measurements. © 2021, CC BY-NC-ND.

关键词： Stochastic systems

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Manufacturing industry supply chain management based on the ethereum blockchain

Manufacturing industry supply chain management based on the ...

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2019 IEEE International Conferences on Ubiquitous Computing and communications and Data Science and Computational Intelligence and Smart Computing, networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Xu, Zhijun Liu, Yichen Zhang, Jun Song, Zhaoxiong Li, Jun Zhou, Jihua School of Computer Science State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China College of Computer Science Inner Mongolia University Hohhot China Institute of Computing Technology Chinese Academy of Sciences Beijing China Bubi Technologies Beijing China Chongqing Jinmei Communication Chongqing China

ISBN: (纸本)9781728152097

Nowadays the composition and structure of the manufacturing industry supply chain have become increasingly complex. The loss and not-in-time transmission of the supply chain information have aggravated the bullwhip effect. At the same time, due to the lack of reliable information storage, the difficulties of traceability and accountability have also caused supply chain management to fall into the bottleneck. The blockchain has the characteristics of supporting distributed networking, information synchronization among nodes, digital encryption, traceable information and block content that cannot be tampered, which is suitable for use in supply chain, and can provide solution for it. In this paper, a design scheme of integrated platform for information service provided by participants in supply chain and based on Ethereum blockchain is proposed. By using Ethereum smart contracts, the common business involved in the supply chain is realized using blockchain technology, and the key information of supply chain production and circulation is stored on the blockchain to ensure the information cannot be tampered. At the same time, a reputation evaluating method based on smart contracts is used to evaluate the reputation of enterprises in supply chain, which can provide reference for supplier selection among enterprises. © 2019 IEEE.

关键词： Smart contract

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Topology Based Reliable Virtual Network Embedding from a QoE Perspective

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China communications 2018年第10期15卷 38-50页

作者： Peiying Zhang Sheng Wu Miao Wang Haipeng Yao Yunjie Liu College of Computer & Communication Engineering China University of Petroleum (East China) State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications DFH Satellite Co. Ltd.

In the network virtualization environments, one of the most challenges is how to map the virtual networks(VNs) onto a shared substrate network managed by an infrastructure provider(In P), which is termed as virtual network embedding problem. Prior studies on this issue only emphasize on maximizing the revenue or minimizing the energy consumption while ignoring the reliability requirements of end-users. In our work, we incorporate the reliability probability into the virtual network embedding process with an aim to improve the Qo S/Qo E of end users from a new perspective. We devised two novel reliable virtual network embedding algorithms called RRW-Max Match and RDCC-VNE based on RW-Max Match and DCC-VNE, respectively. Extensive simulations demonstrated that the efficiency of our proposed algorithms is better than those of two primitive algorithms in terms of the reliability demands, the acceptance ratio of virtual networks and the long-term average revenue.

关键词： virtual network embedding reliability probability node mapping procedure link mapping procedure node ranking metric

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

引用

arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses

arXiv

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arXiv 2025年

作者： Kang, Yingkai Kang, Jiawen Wen, Jinbo Zhang, Tao Yang, Zhaohui Niyato, Dusit Zhang, Yan School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking Zhejiang University Hangzhou310007 China College of Computing and Data Science Nanyang Technological University Singapore Department of Informatics University of Oslo the Simula Research Laboratory Norway

Vehicular metaverses are an emerging paradigm that merges intelligent transportation systems with virtual spaces, leveraging advanced digital twin and Artificial Intelligence (AI) technologies to seamlessly integrate vehicles, users, and digital environments. In this paradigm, vehicular AI agents are endowed with environment perception, decision-making, and action execution capabilities, enabling real-time processing and analysis of multi-modal data to provide users with customized interactive services. Since vehicular AI agents require substantial resources for real-time decision-making, given vehicle mobility and network dynamics conditions, the AI agents are deployed in RoadSide Units (RSUs) with sufficient resources and dynamically migrated among them. However, AI agent migration requires frequent data exchanges, which may expose vehicular metaverses to potential cyber attacks. To this end, we propose a reliable vehicular AI agent migration framework, achieving reliable dynamic migration and efficient resource scheduling through cooperation between vehicles and RSUs. Additionally, we design a trust evaluation model based on the theory of planned behavior to dynamically quantify the reputation of RSUs, thereby better accommodating the personalized trust preferences of users. We then model the vehicular AI agent migration process as a partially observable markov decision process and develop a Confidence-regulated Generative Diffusion Model (CGDM) to efficiently generate AI agent migration decisions. Numerical results demonstrate that the CGDM algorithm significantly outperforms baseline methods in reducing system latency and enhancing robustness against cyber attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Towards phase balancing using energy storage

arXiv

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arXiv 2020年

作者： Hashmi, Md Umar Horta, Jose Pereira, Lucas Lee, Zachary Busic, Ana Kofman, Daniel INRIA and the Computer Science Dept. of Ecole Normale Supérieure CNRS PSL Research University Paris France Laboratory of Information Networking and Communication Sciences Telecom ParisTech Paris France ITI LARSyS Técnico Lisboa and *** Funchal Portugal Electrical Engineering Department California Institute of Technology PassdenaCA United States

Ad-hoc growth of single-phase-connected distributed energy resources, such as solar generation and electric vehicles, can lead to network unbalance with negative consequences on the quality and efficiency of electricity supply. Case-studies are presented for a substation in Madeira, Portugal and an EV charging facility in Pasadena, California. These case studies show that phase imbalance can happen due to a large amount of distributed generation (DG) and electric vehicle (EV) integration. We conducted stylized load-flow analysis on a radial distribution network using an openDSS-based simulator to understand such negative effects of phase imbalance on neutral and phase conductor losses, and in voltage drop/rise. We evaluate the integration of storage in the distribution network as a possible solution for mitigating effects caused by imbalance. We present control architectures of storage operation for phase balancing. Numerically we show that relatively small-sized storage (compared to unbalance magnitude) can significantly reduce network imbalance. We identify the end node of the feeder as the best location to install storage. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric vehicles

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Primitives towards verifiable computation： a survey

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Frontiers of computer Science 2018年第3期12卷 451-478页

作者： Haseeb AHMAD Licheng WANG Haibo HONG Jing LI Hassan DAWOOD Manzoor AHMED Yixian YANG State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou 310018 China Department of Computer Engineering University of Engineering and Technology Taxila 47050 Pakistan Future Internet & Communication Lab Department of Electronic Engineering Tsinghua University Beijing 100084 China

Verifiable computation （VC） paradigm has got the captivation that in real term is highlighted by the concept of third party computation. In more explicate terms, VC allows resource constrained clients/organizations to securely outsource expensive computations to untrusted service providers, while acquiring the publicly or privately verifiable results. Many mainstream solutions have been proposed to address the diverse problems within the VC domain. Some of them imposed assumptions over performed computations, while the others took advantage of interactivity /non-interactivity, zero knowledge proofs, and arguments. Further proposals utilized the powers of probabilistic checkable or computationally sound proofs. In this survey, we present a chronological study and classify the VC proposals based on their adopted domains. First, we provide a broader overview of the theoretical advancements while critically analyzing them. Subsequently, we present a comprehensive view of their utilization in the state of the art VC approaches. Moreover, a brief overview of recent proof based VC systems is also presented that lifted up the VC domain to the verge of practicality. We use the presented study and reviewed resuits to identify the similarities and alterations, modifications, and hybridization of different approaches, while comparing their advantages and reporting their overheads. Finally, we discuss implementation of such VC based systems, their applications, and the likely future directions.

关键词： verifiable computation cloud computation interactive non-interactive zero knowledge probabilisticcheckable proofs computationally sound proofs

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A novel sentence similarity model with word embedding based on convolutional neural network

A novel sentence similarity model with word embedding based ...

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作者： Yao, Haipeng Liu, Huiwen Zhang, Peiying State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China College of Computer and Communication Engineering China University of Petroleum Qingdao China

In this paper, we propose an effective model for the similarity metrics of English sentences. In the model, we first make use of word embedding and convolutional neural network (CNN) to produce a sentence vector and then leverage the information of the sentence vector pair to calculate the score of sentence similarity. Considering the case of long-range semantic dependencies between words, we propose a novel method transforming word embeddings to construct the three-dimensional sentence feature tensor. In addition, we incorporate the k-max pooling into the convolutional neural network to adapt to variable lengths of input sentences. The proposed model requires no external resource such as WordNet and parse tree. Meanwhile, it consumes very little time for training. Finally, we carried out extensive simulations to evaluate the performance of our model compared with other state-of-the-art works. Experimental results on SemEval 2014 task (SICK test corpus) indicated that our model can achieve a good performance in the terms of Pearson correlation coefficient, Spearman correlation coefficient, and mean squared errors. Furthermore, experimental results on Microsoft research paraphrase identification (MSRP) indicated that our model can achieve an excellent performance in the terms of F1 and Accuracy. Copyright © 2018 John Wiley & Sons, Ltd.

关键词： Embeddings

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