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Intelligent reflecting surface-assisted federated learning in multi-platoon collaborative networks

Digital Communications and Networks 2023年第3期9卷 628-637页

作者： Xiaoting Ma junhui Zhao Jieyu Liao Ziyang Zhang School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China China Telecom Corporation Limited Research Institute Beijing102209China School of Information Engineering East China Jiaotong UniversityNanchang330013China

Inspired by mobile edge computing(MEC),edge learning has gained a momentum by directly performing model training at network edge without sending massive data to a centralized data center.However,the quality of model training will be affected by the limited communication and computing resources of network edge.In this paper,how to improve the training performance of a federated learning system aided by intelligent reflecting surface(IRS)over vehicle platooning networks is studied,where multiple platoons train a shared federated learning model.Multi-platoon cooperation can alleviate the pressure of data processing caused by the limited computing resources of single platoon.Meanwhile,IRS can enhance the inter-platoon communication in a cost-effective and energy-efficient manner.Firstly,the federated learning optimization problem of maximizing the learning accuracy is formulated by jointing platoon scheduling,bandwidth allocation and phase shifts at the IRS to maximize the number of scheduled platoon.Specif-ically,in the proposed learning architecture each platoon updates the learning model with its own data and uploads it to the global model through IRS-based wireless networks.Then,a method based on sequential optimization algorithm(SOA)and a group-based optimization method are analyzed for single IRS aided and large-scale IRS aided commu-nication,respectively.Finally,a platoon scheduling scheme is designed based on the communication reliability and computing reliability of platoons.Simulation results demonstrate that large-scale IRS assisted communication can effectively improve the reliability of multi-user communication networks.The scheduling scheme based on learning reliability balances the communication performance and computing performance of platoons.

关键词： Vehicle platooning networks Federated learning Intelligent reflecting surface Platoon scheduling Resource allocation

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Mobile Edge Computing Servers Deployment with Improved Genetic Algorithm in Cellular Internet of Things

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中国通信:英文版 2023年第9期20卷 215-226页

作者： Huan Zhang junhui Zhao Lihua Yang Ziyang Zhang School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Information Engineering East China Jiaotong UniversityNanchang 330013China Lenovo Research Lenovo Group LimitedBeijing 100094China

Mobile edge computing(MEC)provides services to devices and reduces latency in cellular internet of things(IoT)networks.However,the challenging problem is how to deploy MEC servers economically and efficiently.This paper investigates the deployment problem of MEC servers of the real-world road network by employing an improved genetic algorithm(GA)scheme.We first use the threshold-based K-means algorithm to form vehicle clusters according to their locations.We then select base stations(BSs)based on clustering center coordinates as the deployment locations set for potential MEC servers.We further select BSs using a combined simulated annealing(SA)algorithm and GA to minimize the deployment cost.The simulation results show that the improved GA deploys MEC servers effectively.In addition,the proposed algorithm outperforms GA and SA algorithms in terms of convergence speed and solution quality.

关键词： deployment of MEC servers IoT im-proved GA cost

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Secure and Efficient Computing Resource Management in Blockchain-Based Vehicular Fog Computing

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China Communications 2021年第4期18卷 115-125页

作者： Ming Kong junhui Zhao Xiaoke Sun Yiwen Nie School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Information Engineering East China Jiaotong UniversityNanchang 330013China

In vehicular fog computing(VFC),the resource transactions in the Internet of Vehicles(IoV)have become a novel resource management scheme that can improve system resource utilization and the quality of vehicle services.In this paper,in order to improve the security and fairness of resource transactions,we design a blockchain-based resource management scheme for VFC.First,we propose the concept of resource coin(RC)and develop a blockchain-based secure computing reource trading mechanism in terms of RC.As a node of the blockchain network,the roadside unit(RSU)participates in verifying the legitimacy of transactions and the creation of new blocks.Next,we propose a resource management scheme based on contract theory,encouraging parked vehicles to contribute computing resource so that RSU could complete proof of work(PoW)quickly,improve the success probability of block creation and get RC rewards.We use the gradient descent method to solve the computing resource utilization that can maximize the RC revenue of RSUs and vehicles during the block creation.Finally,the performance of this model is validated in simulation result and analysis.

关键词： blockchain vehicular fog computing resource management contract theory

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Energy-Efficient Computation Offloading and Resource Allocation in Fog Computing for Internet of Everything

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China Communications 2019年第3期16卷 32-41页

作者： Qiuping Li junhui Zhao Yi Gong Qingmiao Zhang School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Information Engineering East China Jiaotong UniversityNanchang 330013China Shenzhen Engineering Laboratory of Intelligent Information Processing for IoT Southern University of Science and TechnologyShenzhen 518055China

With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases.

关键词： fog computing cloud computing resource allocation computation offloading IoE

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Future 5G-Oriented System for Urban Rail Transit:Opportunities and Challenges

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China Communications 2021年第2期18卷 1-12页

作者： junhui Zhao Jin Liu Lihua Yang Bo Ai Shanjin Ni School of Information Engineering East China Jiaotong UniversityNanchang 330013China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China

As a development direction of urban rail transit system,the train autonomous circumambulate system(TACS)can operate in a safer,more efficient,and more economical mode.However,most urban rail transit systems transmit signals through industrial,scientific,and medical(ISM)frequency bands or narrow frequency bands,which cannot meet the requirements of TACS.As a promising solution,the 5th generation(5G)mobile communication provides more services for the future urban rail transit systems,and covers the shortages of exiting communication technologies in terms of capacity and reliability.In this paper,we first briefly review the research status of current train control system and introduce its limitations.Next,we propose a novel network architecture,and present new technologies and requirements of the proposed architecture for TACS.Some potential challenges are then discussed to give insights for further research of TACS.

关键词： urban rail transit train autonomous circumambulate system(TACS) prospective review the 5th-generation(5G) train to train(T2T)communication reinforcement learning(RL)

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Energy-Efficient UAV Trajectory Design for Backscatter Communication: A Deep Reinforcement Learning Approach

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China Communications 2020年第10期17卷 129-141页

作者： Yiwen Nie junhui Zhao Jun Liu Jing Jiang Ruijin Ding School of Electronic and Information Engineering Beijing Jioatong UniversityBeijing 100044China School of Information Engineering East China Jiaotong UniversityNanchang 330013China Institute of Network Sciences and Cyberspace Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology(BNRist) Beijing 100084China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and TelecommunicationsXi’an 710121China Institute for Artificial Intelligence Tsinghua University(THUAI)Beijing 100084China State Key Laboratory of Intelligent Technologies and Systems Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology(BNRist) Department of AutomationTsinghua UniversityBeijing 100084China

Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC system thanks to their high mobility and flexibility.In this paper,we investigate the problem of energy efficiency(EE)for an energy-limited backscatter communication(BC)network,where backscatter devices(BDs)on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor.Specifically,we first reformulate the EE optimization problem as a Markov decision process(MDP)and then propose a deep reinforcement learning(DRL)algorithm to design the UAV trajectory with the constraints of the BD scheduling,the power reflection coefficients,the transmission power,and the fairness among BDs.Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.

关键词： unmanned aerial vehicle(UAV) trajectory design backscatter communication deep reinforcement learning energy-efficient

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Signal Path Reckoning Localization Method in Multipath Environment

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China Communications 2017年第3期14卷 182-189页

作者： junhui Zhao Lei Li Hao Zhang Yi Gong School of Electronic and Information Engineering Beijing Jiaotong University School of Information Engineering East China Jiaotong University Department of Electrical and Electronic Engineering South University of Science and Technology of China

In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.

关键词：信号路径多径环境定位方法推算方法地理信息系统无线定位优化算法定位精度

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Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

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China Communications 2024年第5期21卷 113-122页

作者： Qiu Wanqing Zhang Qingmiao zhao junhui Yang Lihua School of Information Engineering East China Jiaotong UniversityNanchang 330013China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

关键词： extreme learning machine fingerprinting localization indoor localization machine learning particle swarm optimization

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High-performance compensation scheme for frequency-dependent IQ imbalances in OFDM transmitter and receiver

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Journal of Systems Engineering and Electronics 2013年第2期24卷 204-208页

作者： Yan Liang Feng Shu Yijin Zhang junhui Zhao School of Electronic Engineering and Optoelectronic Technology Nanjing University of Science and Technology College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications National Mobile Communications Research Laboratory Southeast University School of Electronic and Information Engineering Beijing Jiaotong University

An efficient compensation scheme combining a timedomain Gaussian elimination（GE） channel estimator and a frequency-domain GE equalizer is proposed for orthogonal frequency division multiplexing（OFDM） systems with frequencydependent in-phase and quadrature-phase（IQ） imbalances at both transmitter and receiver.Compared with the traditional least square and least mean square compensation schemes,the proposed compensation scheme achieves the same bit error rate as the ideal IQ branches by using only two training OFDM symbols instead of about 20 OFDM symbols.

关键词： in-phase and quadrature-phase（IQ） imbalance time domain frequency domain least square least mean square Gaussian elimination（GE）.

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Effects of specific doses of E-beam irradiation which inactivated SARS-CoV-2 on the nutrition and quality of Atlantic salmon

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Food Science and Human Wellness 2023年第4期12卷 1351-1358页

作者： Huilin Yu junhui Zhang Yan zhao Honghao Li Yixuan Chen Jiajin Zhu Department of Food Science and Nutrition Zhejiang UniversityHangzhou 310058China

The contamination of Atlantic salmon with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)has impeded the development of the cold-chain food industry and posed possible risks to the population.Electron beam(E-beam)irradiation under 2,4,7,and 10 kGy can effectively inactivate SARS-CoV-2 in cold-chain seafood.However,there are few statistics about the quality changes of salmon exposed to these irradiation dosages.This work demonstrated that E-beam irradiation at dosages capable of killing SARS-CoV-2 induced lipid oxidation,decreased vitamin A content,and increased some amino acids and ash content.In addition,irradiation altered the textural features of salmon,such as its hardness,resilience,cohesiveness,and chewiness.The irradiation considerably affected the L*,a*,and b*values of salmon,with the L*value increasing and a*,b*values decreasing.There was no significant difference in the sensory evaluation of control and irradiated salmon.It was shown that irradiation with 2−7 kGy E-beam did not significantly degrade quality.The inactivation of SARS-CoV-2 in salmon is advised at a dose of 2 kGy.

关键词： E-beam irradiation Atlantic salmon SARS-CoV-2 Physicochemical properties

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