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IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Xiyao Liu Bo Yang Xiao Feng Huang Kwok Wa Leung Nan Yang Department of Electrical Engineering State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Hong Kong SAR China The Shenzhen Key Laboratory of Millimeter Wave and Wideband Wireless Communications CityU Shenzhen Research Institute Shenzhen City China Comba Network Systems Company Limited Guangzhou City China School of Electronics and Information Technology Sun Yat-sen University Guangzhou City China

ISBN: (纸本)9781665490382

A circularly polarized reconfigurable dielectric resonator (DR) antenna (DRA) is studied. It consists of a square ring slot, a two-layer rectangular DR, and a Y-shaped feeding line with two diodes. Two degenerated fundamental modes are excited by the Y-shaped feeding line. By controlling the states of the reversed diodes in the feedline, the phase difference between the two degenerated modes can be switched between 90° and - 90°, giving left-hand circular polarization (LHCP) or right-hand circular polarization (RHCP). Simulated results show that our reconfigurable DRA has a -10-dB impedance bandwidth and a 3-dB AR bandwidth of 29.5% and 25.6%, respectively. Their common bandwidth is 25.6%. It also has broadside radiation patterns with an average gain of ~6.1 dBic in each state.

关键词： Antenna measurements Polarization Bandwidth Switches Reflector antennas Control systems Dielectric resonator antennas

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Federated Quantile Regression over networks

Federated Quantile Regression over Networks

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International wireless communications and Mobile Computing Conference, IWCMC

作者： Liqi Huang Xin Wei Peikang Zhu Yun Gao Mingkai Chen Bin Kang Key Laboratory of Broadband Wireless Communications and Sensor Network Technology Ministry of Education (NUPT) Nanjing China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

In order to solve the issue of isolated data islands and data security and personal privacy in the development of artificial intelligence, federated machine learning effectively solves the problem of sharing knowledge while protecting user privacy and data security. In a wireless sensor network, a secure learning framework is particularly needed, so that each sensor node can jointly learn knowledge without leaking local node data. Compared to traditional regression analysis algorithms, quantile regression can more fully describe the relationship between response values and its covariates by estimating conditional quantile sequences rather than a single value (such as the mean). In this paper, we propose a quantile regression federated learning framework that applies quantile regression with federated learning frameworks to wireless sensor networks, studies the performance of the algorithms, and the effectiveness of the algorithms is verified by simulations.

关键词： Data models Machine learning Encryption Servers Computational modeling

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Secrecy Outage Probability Analysis Based on Cognitive Decode-and-Forward Relaying

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Computers, Materials & Continua 2020年第3期62卷 1387-1395页

作者： Ruoyu Su Xiaojun Sun Fei Ding Dengyin Zhang Hongbo Zhu M.I.M.Wahab Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Tele-CommunicationsNanjing210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China School of Information Science and Engineering Southeast UniversityNanjing210018China Department of Electrical and Computer Engineering Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandSt.John’sA1B 3X5Canada Department of Mechanical and Industrial Engineering Ryerson UniversityToronto M5B 2K3Canada

wireless communications have to face to several different security issues in practice due to the nature of *** information theory is well known to provide efficient approaches to address security issues in wireless communications,which attracts much attention in both industry and academia in recent *** this paper,inspired by information theory,we study the outage probability of the opportunistic relay selection based on cognitive decode-and-forward relaying with the secrecy ***,the closed-form expression of the outage probability is ***,the asymptotic performance evaluation on the basis of the analytical results is *** simulation results show that the relay selection can reduce the outage probability in accordance with our theoretical analysis.

关键词： Secure communication average secrecy rate relay selection probability density function

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Compressed network in network Models for Traffic Classification

Compressed Network in Network Models for Traffic Classificat...

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IEEE Conference on wireless communications and networking

作者： Min Lu Bin Zhou Zhiyong Bu Kecheng Zhang Zhenhua Ling Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology Shanghai China University of Chinese Academy of Sciences Beijing China Key Laboratory of Wireless Sensor Network and Communications SIMIT CAS Shanghai China University of Science and Technology of China Hefei China

Accurate traffic classification is critical for network QoS provisioning and cyberspace security. Recently, classifying different traffic using convolutional neural networks (CNN) has achieved high accuracy. However, these large CNN models have millions of parameters, which are not suitable for edge computing hardware deployment. In this work, we propose a compressed network in network (NIN) model for traffic identification. A stepwise pruning and knowledge distillation (KD) is designed for training the compressed model, which aims at reducing storage and computing resources. Our method is validated with the public ISCX VPN-nonVPN traffic dataset. Experimental results show that without degrading classification accuracy, our minimum model can save more than 50% of the number of parameters and 30% of the computation time comparing with the uncompressed NIN model. The test set average F₁ score of 0.9805 of the minimum model is higher than that of the state-of-the-art model, which is a CNN model.

关键词： Training Computational modeling Conferences Cyberspace Quality of service Hardware Security

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On the Performance of Delay Line Based OAM communications

On the Performance of Delay Line Based OAM Communications

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2021 IEEE Global communications Conference (GLOBECOM)

作者： Wei Yu Bin Zhou Zhiyong Bu Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology Shanghai China ShanghaiTech University Shanghai China SIMIT CAS Key Laboratory of Wireless Sensor Network and Communications Shanghai China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728181059

Delay line based uniform circular antenna array (UCA) is commonly used for orbital angular momentum (OAM) generation. It is known that delay line is preferred for Multiple-input Multiple-output(MIMO) beamforming than the ideal phase shifter(PS), as the latter causes the problem of beam squint. Different from traditional MIMO beamforming, we theoretically prove in this paper that ideal PS is preferred for UCA based OAM communications, while delay line causes vortex distortion. Specifically, through the derivation of baseband equivalent model, we demonstrate that vortex distortion of delay line is a time-varying process, which is affected by OAM mode and signal bandwidth. To verify our analysis, we first simulate instantaneous distortion, and then simulate isolation and spectrum efficiency in different OAM mode and bandwidth configurations. The simulation results indicate that for UCA based OAM commu-nication systems using delay line, higher OAM mode and higher bandwidth greatly limit mode isolation and spectrum efficiency. Our research also applies to delay line based customized OAM antennas, such as helicoidal parabolic antenna, traveling wave circular loop antenna and so on. The research result is of great significance to OAM based wireless communications.

关键词： wireless communication Baseband Array signal processing Simulation Bandwidth Delay lines Distortion

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Reputation-Based Regional Federated Learning for Knowledge Trading in Blockchain-Enhanced IoV

Reputation-Based Regional Federated Learning for Knowledge T...

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IEEE Conference on wireless communications and networking

作者： Yue Zou Fei Shen Feng Yan Jing Lin Yunzhou Qiu Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Information Management Shanghai Lixin University of Accounting and Finance Shanghai

The Internet of Vehicles (IoV) aims to perceive, compute, and process environmental data in a collaborative manner. Previous works focus on data sharing between vehicles, but a large amount of data will lead to redundant transmission and network congestion. In addition, security and privacy issues prevent these nodes from participating in the sharing process. Knowledge is extracted from data through machine learning (ML) and shared in the form of small-scale well-trained model parameters, which improves collaborative learning more effectively and relieves network pressure. While traditional ML algorithms are not suitable for distributed IoV with local characteristics. Based on this, this paper first divides the vehicles into multiple regions and proposes a Regional Federated Learning (RFL) framework, in which all regions maintain their own learning models, i.e. knowledge. We design a reputation mechanism to measure the reliability of vehicles participating in RFL. To address the security challenges brought by the untrusted centralized trading market, we propose a blockchain-enhanced knowledge trading framework, in which an authorized market agency coordinates the trading quickly. We model the optimal pricing mechanism as a non-cooperative game, taking into account the competition among all knowledge providers. Numerical simulation shows that the proposed reputation mechanism improves the accuracy of knowledge up to 18%, and the optimal knowledge pricing mechanism effectively increases the utility of market.

关键词： Knowledge engineering Smart contracts Collaboration Pricing Games Collaborative work Numerical simulation

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Deep-broad Learning System for Traffic Flow Prediction toward 5G Cellular wireless network

Deep-broad Learning System for Traffic Flow Prediction towar...

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International wireless communications and Mobile Computing Conference, IWCMC

作者： Mingzi Chen Xin Wei Yun Gao Liqi Huang Mingkai Chen Bin Kang Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Broadband Wireless Communications and Sensor Network Technology Ministry of Education (NUPT) Nanjing China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

Nowadays, accurate traffic flow prediction toward 5G cellular wireless network has become an indispensable part for future artificial intelligence (AI)-assisted network. Meanwhile, low delay communication is also the essential part in the upcoming 5G era. However, traditional deep learning models applied in traffic flow prediction have many drawbacks, such as too much running time and computational resources. To tackle these issues, especially jointly considering effectiveness and efficiency, we design a deep-broad learning system (DBLS) for traffic flow prediction. Specifically, based on broad learning system (BLS), we firstly adopt deep representative learning to extract meaningful information from raw data in mapped feature nodes. Then, to further improve the performance of prediction, we add some other nodes i.e., enhancement nodes generated from mapped features as extra inputs to enhance the representative capability. Finally, taking mapped features nodes and enhancement nodes as inputs of the last-layer neural network, we apply ridge regression to compute the final weights quickly. Experimental results demonstrate that our proposed DBLS can make full use of advantages of both deep neural network and traditional BLS to increase the accuracy of traffic flow prediction, meanwhile, maintaining low complexity and running time.

关键词： 5G mobile communication Learning systems Feature extraction Machine learning Predictive models Time series analysis Neural networks

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Energy Efficiency Optimization in Heterogeneous networks Based on Deep Reinforcement Learning

Energy Efficiency Optimization in Heterogeneous Networks Bas...

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IEEE International Conference on communications Workshops, ICC

作者： Daoping Shi Feng Tian Shengchen Wu Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing Universityof Posts and Telecommunications) Ministry of Education Nanjing China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

To meet the rapid increasing requirement of service and application of communication, heterogeneous wireless networks with the macrocell and the femtocells are considered in this paper. How to deal with resource allocation efficiently and dynamically in heterogeneous wireless networks puts forward to an urgent problem. In this paper, we aim to maximize the overall network's energy efficiency, where multiple femto base stations are randomly and densely distributed in the coverage of macro base station. We first construct an EE model and formulate the problem for optimization. Then we propose a Nature DQN algorithm in deep reinforcement learning to solve it with power discretization. Finally, in simulations, it demonstrates that the proposed Nature DQN can not only achieve better energy efficiency than Q-learning and water-filling but also accelerate the convergence.

关键词： Optimization Resource management Macrocell networks wireless networks Machine learning Femtocells Interference

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High-Efficiency Resonant Beam Charging and Communication

arXiv

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arXiv 2021年

作者： Bai, Yunfeng Liu, Qingwen Wang, Xin Gou, Yudan Zhou, Bin Bu, Zhiyong The College of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory for Information Science of Electromagnetic Waves Department of Communication Science and Engineering Fudan University Shanghai200433 China The College of Electronics and Information Engineering Sichuan University Chengdu610065 China The Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China

With the development of Internet of Things (IoT), demands of power and data for IoT devices increase drastically. In order to resolve the supply-demand contradiction, simultaneous wireless information and power transfer (SWIPT) has been envisioned as an enabling technology by providing high-power energy transfer and high-rate data delivering concurrently. In this paper, we introduce a high-efficiency resonant beam (RB) charging and communication scheme. The scheme utilizes the semiconductor materials as gain medium, which has a better energy absorption capacity compared with the traditional solid-state one. Moreover, the telescope internal modulator (TIM) are adopted in the scheme which can concentrate beams to match the gain size, reducing the transmission loss. To evaluate the scheme SWIPT performance, we establish an analytical model and study the influence factors of its beam transmission, energy conversion, output power, and spectral efficiency. Numerical results shows that the proposed RB system can realize 16 W electric power output with 11 % end-to-end conversion efficiency, and support 18 bit/s/Hz spectral efficiency for communication. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical communication

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A coalitional game based user access algorithm in energy-harvesting small cell networks 18

A coalitional game based user access algorithm in energy-har...

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2018 18th IEEE International Conference on Communication technology, ICCT 2018

作者： Wang, Xueting Izhu, Q. Nanjing University of Posts and Telecommunications Jiangsu Province Key Laboratory of Wireless Communications Key Laboratory on Wideband Wireless Communications and Sensor Network Technology of Ministry of Education Nanjing China

ISBN: (纸本)9781538676349

To improve the users' satisfaction of downloading files, a cache based resource allocation algorithm in energy-harvesting small cell networks is proposed, which is established as a coalitional game and users try to switch between coalitions with satisfaction as utility function. The switch is accepted once the utilities of user and coalition after switching satisfies the switch orders. This algorithm also allows multi users to be served by a same small cell sharing the same channel using Non-orthogonal Frequency Division Multiple Access (NOMA). The simulation results show that our novel algorithm performs better on user satisfaction. © 2018 IEEE.

关键词： Energy harvesting

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