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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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Gradient Amplification: An efficient way to train deep neural networks

arXiv

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arXiv 2020年

作者： Basodi, Sunitha Ji, Chunyan Zhang, Haiping Pan, Yi Department of Computer Science Georgia State University AtlantaGA30302 United States Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences ShenzhenGuangdong518 055 China

Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address these challenges one of which is to increase the depth of the neural networks. Such deeper networks not only increase training times, but also suffer from vanishing gradients problem while training. In this work, we propose gradient amplification approach for training deep learning models to prevent vanishing gradients and also develop a training strategy to enable or disable gradient amplification method across several epochs with different learning rates. We perform experiments on VGG-19 and resnet (Resnet-18 and Resnet-34) models, and study the impact of amplification parameters on these models in detail. Our proposed approach improves performance of these deep learning models even at higher learning rates, thereby allowing these models to achieve higher performance with reduced training time. Copyright © 2020, The Authors. All rights reserved.

关键词： Backpropagation

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Identification of new disease genes from protein–protein interaction network

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Journal of Ambient Intelligence and Humanized Computing 2018年 1-9页

作者： Masood, M. Mohamed Divan Manjula, D. Sugumaran, Vijayan Department of Computer Science and Engineering Anna University Chennai India Center for Data Science and Big Data Analytics Department of Decision and Information Sciences School of Business Administration Oakland University RochesterMI48309 United States

Human beings are susceptible to diseases caused by a variety of reasons. The causal factors for diseases include microorganisms (viruses and bacteria), environmental factors and largely, genetics. As most diseases are caused by the genes of a particular living organism, it is necessary to analyze the genetic characteristics to treat the disease effectively. The genetics analysis is done in this research using protein–protein interaction network (PPI), which contains information on a gene’s interaction with other genes of a particular living organism. Using the PPI network, new genes for a particular disease can be discovered by applying the shortest path algorithm on the PPI network, given that genes reside next to one another in the network. Consequently, the shortest path algorithm that is executed on the PPI network will retrieve the most similar genes that are related to a particular disease. From the retrieved candidate genes, irrelevant genes are filtered out and the remaining are taken as novel genes. © 2018 Springer-Verlag GmbH Germany, part of Springer Nature

关键词： Genes

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WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

arXiv

引用

arXiv 2021年

作者： Luo, Xiangde Liao, Wenjun Xiao, Jianghong Chen, Jieneng Song, Tao Zhang, Xiaofan Li, Kang Metaxas, Dimitris N. Wang, Guotai Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital and Institute Sichuan Cancer Center Chengdu China Department of Radiation Oncology Cancer Center West China Hospital Sichuan University Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China School of Medicine University of Electronic Science and Technology of China Chengdu China Department of Computer Science Johns Hopkins University Baltimore United States SenseTime Research Shanghai China West China Hospital-SenseTime Joint Lab West China Biomedical Big Data Center Sichuan University Chengdu China Department of Computer Science Rutgers University PiscatawayNJ United States

Whole abdominal organ segmentation is important in diagnosing abdomen lesions, radiotherapy, and follow-up. However, oncologists’ delineating all abdominal organs from 3D volumes is time-consuming and very expensive. Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation. In this work, we establish a new large-scale Whole abdominal ORgan dataset (WORD) for algorithm research and clinical application development. This dataset contains 150 abdominal CT volumes (30495 slices). Each volume has 16 organs with fine pixel-level annotations and scribble-based sparse annotations, which may be the largest dataset with whole abdominal organ annotation. Several state-of-the-art segmentation methods are evaluated on this dataset. And we also invited three experienced oncologists to revise the model predictions to measure the gap between the deep learning method and oncologists. Afterwards, we investigate the inference-efficient learning on the WORD, as the high-resolution image requires large GPU memory and a long inference time in the test stage. We further evaluate the scribble-based annotation-efficient learning on this dataset, as the pixel-wise manual annotation is time-consuming and expensive. The work provided a new benchmark for the abdominal multi-organ segmentation task, and these experiments can serve as the baseline for future research and clinical application development. © 2021, CC BY.

关键词： Large dataset

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Robust beamforming design for covert communications

arXiv

引用

arXiv 2021年

作者： Ma, Shuai Zhang, Yunqi Li, Hang Lu, Songtao Al-Dhahir, Naofal Zhang, Sha Li, Shiyin School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Shenzhen Research Institute of Big Data Shenzhen Guangdong518172 China IBM Thomas J. Watson Research Center Yorktown HeightsNY10598 United States Electrical and Computer Engineering Department University of Texas at Dallas DallasTX75080 United States Shenzhen Institute of Radio Testing and Tech Shenzhen518000 China

In this paper, we consider a common unicast beamforming network where Alice utilizes the communication to Carol as a cover and covertly transmits a message to Bob without being recognized by Willie. We investigate the beamformer design of Alice to maximize the covert rate to Bob when Alice has either perfect or imperfect knowledge about Willie’s channel state information (WCSI). For the perfect WCSI case, the problem is formulated under the perfect covert constraint, and we develop a covert beamformer by applying semidefinite relaxation and the bisection method. Then, to reduce the computational complexity, we further propose a zero-forcing beamformer design with a single iteration processing. For the case of the imperfect WCSI, the robust beamformer is developed based on a relaxation and restriction approach by utilizing the property of Kullback-Leibler divergence. Furthermore, we derive the optimal decision threshold of Willie, and analyze the false alarm and the missed detection probabilities in this case. Finally, the performance of the proposed beamformer designs is evaluated through numerical experiments. © 2021, CC BY.

关键词： Channel state information

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On PID control for synchronization of complex dynamical network with delayed nodes

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Science China(Technological Sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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Lymgradingnet: Follicular Lymphoma Grading Guided by Fusion Strategy with Pet and Ct

SSRN

引用

SSRN 2024年

作者： Qian, Chunjun He, Lulu Jiang, Qiuhui Zhou, Hang Jiang, Chong Jiang, Zekun Meng, Xianglian Teng, Yue Ding, Chongyang Xu, Bing Li, Xin Ni, Xinye Hertfordshire College Changzhou Institute of Technology NO. 666 Liaohe Road Xinbe District Jiangsu Changzhou213032 China Department of Hematology The First Affiliated Hospital of Xiamen University Institute of Hematology School of Medicine Xiamen University No. 55 Zhenhai Road Siming District Fujian Xiamen361003 China Department of Nuclear Medicine Qilu Hospital of Shandong University 107 Wenhua West Road Lixia District Shandong Jinan250012 China Department of Nuclear Medicine West China Hospital Sichuan University No. 37 Guoxue Alley Wuhou District Sichuan Chengdu610041 China West China Biomedical Big Data Center West China Hospital Sichuan University No. 37 Guoxue Alley Wuhou District Sichuan Chengdu610041 China School of Computer Science and Information Engineering Changzhou Institute of Technology NO. 666 Liaohe Road Xinbe District Jiangsu Changzhou213032 China Department of Nuclear Medicine Nanjing Drum Tower Hospital Affiliated Hospital of Nanjing University Medical School NO. 321 Zhongshan Road Gulou District Jiangsu Nanjing210008 China Department of Nuclear Medicine First Affiliated Hospital of Nanjing Medical University Jiangsu Province Hospital NO. 300 Guangzhou Road Gulou District Jiangsu Nanjing210028 China Changzhou NO.2 People’s Hospital Nanjing Medical University Jiangsu Province Engineering Research Center of Medical Physics Center of Medical Physics Nanjing Medical University No. 68 Gehu Middle Road Wujin District Jiangsu Nanjing213003 China Nanjing University China

Follicular lymphoma (FL) is a non-Hodgkin lymphoma and an indolent B-cell lymphoproliferative disorder of transformed follicular center B cells. In the diagnosis, FL should be graded by counting the number of centroblasts in the pathological image, which is time-consuming. This study proposes a FL grading method based on the PET and CT images. We propose a 3D-DDcGAN to fuse the simultaneously collected PET and CT images. In the 3D-DDcGAN, the surface-level measurement and chrominance information constraint are considered to construct the loss function. Then, due to the uncertainty between the FL grades I and II, the BayesianResNet (ResNet is improved by introducing Bayes' theorem) is adopted for the FL grading. Our method is trained and tested on mixed data consisting of FL grades I-III and DLBCL. Finally, the evaluation metrics for our method are accuracy 0.871, precision 0.875, recall 0.777, macro-averaged F1-score 0.816, and micro-averaged F1-score 0.866. Through comparative experiments, we find that appropriate image fusion methods are helpful for FL grading in PET-CT images, which may be extended to the other diseases' grading. The method based on deep learning and medical imaging will help assist in disease grading, identifying the FL subtypes, and guiding personalized treatment plans. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Cover patches: A general feature extraction strategy for spoofing detection

Cover patches: A general feature extraction strategy for spo...

引用

作者： Cai, Guorong Su, Songzhi Leng, Chengcai Wu, Jipeng Wu, Yundong Li, Shaozi Computer Science and Engineering College Jimei University Xiamen361021 China Department of Intelligence Science and Technology Xiamen University Xiamen360005 China Department of Mathematics Northwest University Xi'an City710127 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China

Face anti-spoofing has attracted many attentions in security applications, such as mobile payment and entrance guard. Until now, face anti-spoofing technique is still a challenging task. Mainstream image-based spoofing algorithms usually use global motion or texture information to distinguish whether an input face is live or fake. However, the performance of these methods are sensitive in light changes, or images acquired from different sensors. The main reason is that spoofed face image always has slight different texture in local areas, such as landmark or salient region of face. To this end, this paper proposes a novel multi-patches feature extraction strategy to detect spoofing. First, a set of patches with specific combination scheme is selected to cover the face image. Second, features such as hand-crafted Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), or deep features are extracted from these patches. Third, all features are combined as the global descriptor of the face image, then fed into an SVM classifier to verify the anti-spoofing detection. Experimental results show that the proposed strategy can effectively enhance the performance, concerning with the accuracy of spoofed face detection in four widely used anti-spoofing databases. © 2018 John Wiley & Sons, Ltd.

关键词： Local binary pattern

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Balancing the Influence of Evolutionary Operators for Global optimization

Balancing the Influence of Evolutionary Operators for Global...

引用

Congress on Evolutionary Computation

作者： Diego Oliva Erick Rodriguez-Esparza Marcella S. R. Martins Mohamed Abd Elaziz Salvador Hinojosa Ahmed A. Ewees Songfeng Lu Universidad de Guadalajara Guadalajara Mexico Federal University of Technology Ponta Grossa Brazil Huazhong University of Science and Technology Wuhan China Department of Computer Damietta University Egypt Hubei Engineering Research Center on Big Data Security School of cyber science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

The proper use of evolutionary operators is crucial to find optimal solutions in a search space. Moreover, the diversity of the population affects the performance of Evolutionary Algorithms (EAs). This article introduces an EA called BWEAD which balances the influence of the operators. The proposal also performs a statistical analysis of the population when the diversity is low and decides which solutions might be replaced. Then BWEAD is able to explore the search space and exploit the prominent regions. The BWEAD has been tested over the CEC2014 set of benchmark functions. The experiments provide competitive results showing an improvement of 30% in 30-dimensional and 50-dimensional functions in comparison with state-of-the-art algorithms, overcoming some addressed instances and providing evidence of its capabilities on complex optimization problems.

关键词： Sociology Statistics Genetic algorithms Evolutionary computation Optimization Space exploration Measurement

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Workload Allocation Based on User Mobility in Mobile Edge Computing

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Journal on big data 2020年第3期2卷 105-115页

作者： Tengfei Yang Xiaojun Shi Yangyang Li Binbin Huang Haiyong Xie Yanting Shen National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data(NEL-PSRPC) BeijingChina National Computer Network Emergency Response Technical Team Coordination Center of China BeijingChina Department of Science and Technology China Electronics Technology Group CorporationBeijingChina School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhouChina University of Science and Technology of China HefeiChina

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all *** offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile ***,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in *** this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single *** obtain the next possible location through the user's past location information,and receive the next access server according to the grid ***,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the *** the experiments,the results demonstrate a high accuracy of our proposed model.

关键词： Edge computing workload allocation social-LSTM context-sensitive user mobility

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