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学者姓名:张延华
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Abstract :
Driven by numerous emerging mobile devices and various Quality-of-Service (QoS) requirements, mobile-edge computing (MEC) has been recognized as a prospective paradigm to promote the computation capability of mobile devices, as well as reduce energy overhead and service latency of applications for the Internet of Things (IoT). However, there are still some open issues in the existing research works: 1) limited network and computing resource; 2) simple or nonintelligent resource management; and 3) ignored security and reliability. In order to cope with these issues, in this article, 6G and blockchain technology are considered to improve network performance and ensure the authenticity of data sharing for the MEC-enabled IoT. Meanwhile, a novel intelligent optimization method named as collective reinforcement learning (CRL) is proposed and introduced, to realize intelligent resource allocation, meet distributed training results sharing, and avoid excessive consumption of system resources. Based on the designed network model, a cloud-edge collaborative resource allocation framework is formulated. By joint optimizing the offloading decision, block interval, and transmission power, it aims to minimize the consumption overheads of system energy and latency. Then, the formulated problem is designed as a Markov decision process, and the optimal strategy can be obtained by the CRL. Some evaluation results reveal that the system performance based on the proposed scheme outperforms other existing schemes obviously.
Keyword :
Internet of Things (IoT) Internet of Things (IoT) Internet of Things Internet of Things Blockchain Blockchain Blockchains Blockchains Resource management Resource management 6G mobile communication 6G mobile communication mobile-edge computing (MEC) mobile-edge computing (MEC) collective reinforcement learning (CRL) collective reinforcement learning (CRL) Computational modeling Computational modeling Servers Servers sixth generation (6G) sixth generation (6G) Optimization Optimization
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GB/T 7714 | Li, Meng , Pei, Pan , Yu, F. Richard et al. Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach [J]. | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (22) : 23115-23129 . |
MLA | Li, Meng et al. "Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach" . | IEEE INTERNET OF THINGS JOURNAL 9 . 22 (2022) : 23115-23129 . |
APA | Li, Meng , Pei, Pan , Yu, F. Richard , Si, Pengbo , Li, Yu , Sun, Enchang et al. Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach . | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (22) , 23115-23129 . |
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Heterogeneous networks (HetNets) with end-to-end (E2E) network slicing are regarded as effective approaches to meet diverse service requirements from vertical industries. Due to the dense deployment of base stations (BSs) and the complicated associations between BSs and E2E network slices (NSs) in the scenario, the handoff problem faces challenges of the huge system state space and handoff action space and the considerable communication overhead. In this paper, we take these issues into account and consider a distributed E2E NS handoff decision framework in the HetNet. A decentralized Markov decision process (DEC-MDP)-based model is formulated for the distributed E2E NS handoff problem, and the jointly observable and random characteristics of the DEC-MDP are analyzed. To obtain a theoretical performance reference, the original distributed E2E NS handoff problem is simplified, and a Nash equilibrium-based performance bound is given. More practically, the multi-agent double deep Q-network-based distributed handoff (MA-DDQN-DH) algorithm with the centralized training and decentralized executing framework is proposed. Simulation results show that the Nash equilibrium-based performance bound is reasonable, and the proposed MA-DDQN-DH algorithm performs well in the comparison.
Keyword :
Heuristic algorithms Heuristic algorithms Bandwidth Bandwidth Distributed network slice handoff Distributed network slice handoff decentralized Markov decision process decentralized Markov decision process Nash equilibrium Nash equilibrium Quality of service Quality of service Real-time systems Real-time systems Costs Costs multi-agent deep reinforcement learning multi-agent deep reinforcement learning Training Training Network slicing Network slicing
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GB/T 7714 | Wu, Wenjun , Yang, Feng , Gao, Yang et al. Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution [J]. | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS , 2022 , 21 (12) : 11222-11236 . |
MLA | Wu, Wenjun et al. "Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution" . | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 21 . 12 (2022) : 11222-11236 . |
APA | Wu, Wenjun , Yang, Feng , Gao, Yang , Wang, Xiaoxi , Si, Pengbo , Zhang, Yanhua et al. Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution . | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS , 2022 , 21 (12) , 11222-11236 . |
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Abstract :
Recently, electric vehicles (EVs) have been widely used under the call of green travel and environmental protection, and diverse requirements for charging are also increasing gradually. In order to ensure the authenticity and privacy of charging information interaction, blockchain technology is proposed and applied in charging station billing systems. However, there are some issues in blockchain itself, including lower computing efficiency of the nodes and higher energy consumption in the consensus process. To handle the above issues, in this paper, combining blockchain and mobile edge computing (MEC), we develop a reliable billing data transmission scheme to improve the computing capacity of nodes and reduce the energy consumption of the consensus process. By jointly optimizing the primary and replica nodes offloading decisions, block size and block interval, the transaction throughput of the blockchain system is maximized, as well as the latency and energy consumption of the system are minimized Moreover, we formulate the joint optimization problem as a Markov decision process (MDP). To tackle the dynamic and continuity of the system state, the reinforcement learning (RL) is introduced to solve the MDP problem. Finally, simulation results demonstrate that the performance improvement of the proposed scheme through comparison with other existing schemes.
Keyword :
billing data interaction billing data interaction mobile edge computing mobile edge computing electric vehicles electric vehicles blockchain blockchain reinforcement learning reinforcement learning
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GB/T 7714 | Ye, Xinyu , Li, Meng , Si, Pengbo et al. Blockchain and MEC-Assisted Reliable Billing Data Transmission over Electric Vehicular Network: An Actor-Critic RL Approach [J]. | CHINA COMMUNICATIONS , 2021 , 18 (8) : 279-296 . |
MLA | Ye, Xinyu et al. "Blockchain and MEC-Assisted Reliable Billing Data Transmission over Electric Vehicular Network: An Actor-Critic RL Approach" . | CHINA COMMUNICATIONS 18 . 8 (2021) : 279-296 . |
APA | Ye, Xinyu , Li, Meng , Si, Pengbo , Yang, Ruizhe , Sun, Enchang , Zhang, Yanhua . Blockchain and MEC-Assisted Reliable Billing Data Transmission over Electric Vehicular Network: An Actor-Critic RL Approach . | CHINA COMMUNICATIONS , 2021 , 18 (8) , 279-296 . |
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Abstract :
采用新型可调谐有源电感的频率可调谐高Q低噪声的带通滤波器
Keyword :
中心频率 中心频率 有源带通滤波器 有源带通滤波器 可调谐有源电感 可调谐有源电感
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GB/T 7714 | 张正 , 张延华 , 温晓伟 et al. 采用新型可调谐有源电感的频率可调谐高Q低噪声的带通滤波器 [J]. | 张正 , 2021 , 44 (1) : 39-45 . |
MLA | 张正 et al. "采用新型可调谐有源电感的频率可调谐高Q低噪声的带通滤波器" . | 张正 44 . 1 (2021) : 39-45 . |
APA | 张正 , 张延华 , 温晓伟 , 那伟聪 , 电子器件 . 采用新型可调谐有源电感的频率可调谐高Q低噪声的带通滤波器 . | 张正 , 2021 , 44 (1) , 39-45 . |
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Abstract :
区块链技术应用于物联网:发展与展望
Keyword :
智能合约 智能合约 数字货币 数字货币 物联网(IoT) 物联网(IoT) 共识机制 共识机制 区块链 区块链
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GB/T 7714 | 叶欣宇 , 李萌 , 赵铖泽 et al. 区块链技术应用于物联网:发展与展望 [J]. | 叶欣宇 , 2021 , 31 (1) : 48-63 . |
MLA | 叶欣宇 et al. "区块链技术应用于物联网:发展与展望" . | 叶欣宇 31 . 1 (2021) : 48-63 . |
APA | 叶欣宇 , 李萌 , 赵铖泽 , 司鹏搏 , 孙阳 , 张延华 et al. 区块链技术应用于物联网:发展与展望 . | 叶欣宇 , 2021 , 31 (1) , 48-63 . |
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Abstract :
对不添加镇流电阻的非均匀发射极条间距的多发射极条异质结双极晶体管(HBT)的射频功率性能和表面温度分布进行了测量,并与常规采用镇流电阻的多发射极条功率HBT进行了比较。实验结果表明,对具有非均匀发射极条间距的多发射极条HBT,采用USQFITMS红外测量系统测得的最高表面温度、温度分布均匀性以及采用射频测量系统测得的射频功率增益和功率附加效率,分别低于、好于和高于具有镇流电阻的多发射极条功率HBT的情况。这些结果的取得,得益于采用非均匀发射极条间距改善了多发射极条HBT的热电正反馈和不同发射极条之间的热耦合,以及摆脱了传统HBT加镇流电阻带来的对射频功率性能的负作用。
Keyword :
双极晶体管 双极晶体管 功率增益 功率增益 功率附加效率 功率附加效率 多指 多指 热稳定性 热稳定性 射频 射频
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GB/T 7714 | 张正 , 张延华 , 金冬月 et al. 免镇流电阻的非均匀发射极指间距设计对多指功率双极晶体管射频功率性能的改善(英文) [J]. | 红外与毫米波学报 , 2021 , 40 (03) : 329-333 . |
MLA | 张正 et al. "免镇流电阻的非均匀发射极指间距设计对多指功率双极晶体管射频功率性能的改善(英文)" . | 红外与毫米波学报 40 . 03 (2021) : 329-333 . |
APA | 张正 , 张延华 , 金冬月 , 那伟聪 , 谢红云 . 免镇流电阻的非均匀发射极指间距设计对多指功率双极晶体管射频功率性能的改善(英文) . | 红外与毫米波学报 , 2021 , 40 (03) , 329-333 . |
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Abstract :
采用可调谐高Q有源电感的高优值VCO的研究
Keyword :
优值 优值 压控振荡器 压控振荡器 可调谐有源电感 可调谐有源电感
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GB/T 7714 | 张正 , 张延华 , 温晓伟 et al. 采用可调谐高Q有源电感的高优值VCO的研究 [J]. | 张正 , 2021 , 44 (2) : 272-277 . |
MLA | 张正 et al. "采用可调谐高Q有源电感的高优值VCO的研究" . | 张正 44 . 2 (2021) : 272-277 . |
APA | 张正 , 张延华 , 温晓伟 , 那伟聪 , 电子器件 . 采用可调谐高Q有源电感的高优值VCO的研究 . | 张正 , 2021 , 44 (2) , 272-277 . |
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Abstract :
人工智能与区块链赋能物联网:发展与展望
Keyword :
去中心化 去中心化 区块链 区块链 机器学习 机器学习 物联网(IoT) 物联网(IoT) 安全性 安全性 人工智能(AI) 人工智能(AI)
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GB/T 7714 | 李萌 , 裴攀 , 孙恩昌 et al. 人工智能与区块链赋能物联网:发展与展望 [J]. | 李萌 , 2021 , 47 (5) : 520-529 . |
MLA | 李萌 et al. "人工智能与区块链赋能物联网:发展与展望" . | 李萌 47 . 5 (2021) : 520-529 . |
APA | 李萌 , 裴攀 , 孙恩昌 , 杨睿哲 , 司鹏搏 , 张延华 et al. 人工智能与区块链赋能物联网:发展与展望 . | 李萌 , 2021 , 47 (5) , 520-529 . |
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Abstract :
Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks; 2) poor efficiency of consensus mechanism in blockchain; and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this article, we integrate mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of the consensus process. Meanwhile, the weighted system cost, including the energy consumption and the computation overhead, are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size, and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes. © 2014 IEEE.
Keyword :
Industrial internet of things (IIoT) Industrial internet of things (IIoT) Energy utilization Energy utilization Reinforcement learning Reinforcement learning Markov processes Markov processes Deep learning Deep learning Blockchain Blockchain Energy efficiency Energy efficiency
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GB/T 7714 | Yang, Le , Li, Meng , Si, Pengbo et al. Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things with Deep Reinforcement Learning [J]. | IEEE Internet of Things Journal , 2021 , 8 (4) : 2318-2329 . |
MLA | Yang, Le et al. "Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things with Deep Reinforcement Learning" . | IEEE Internet of Things Journal 8 . 4 (2021) : 2318-2329 . |
APA | Yang, Le , Li, Meng , Si, Pengbo , Yang, Ruizhe , Sun, Enchang , Zhang, Yanhua . Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things with Deep Reinforcement Learning . | IEEE Internet of Things Journal , 2021 , 8 (4) , 2318-2329 . |
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Abstract :
A compact and reconfigurable low noise amplifier (LNA) is proposed by combining an input transistor, composite transistors with Darlington configuration as the amplification and output transistor, T-type structure composite resistors instead of a simplex structure resistor, a shunt inductor feedback realized by a tunable active inductor (AI), a shunt inductor peaking technique realized by another tunable AI. The division and collaboration among different resistances in the T-type structure composite resistor realize simultaneously input impedance matching, output impedance matching and good noise performance; the shunt feedback and peaking technique using two tunable AIs not only extend frequency bandwidth and improve gain flatness, but also make the gain and frequency band can be tuned simultaneously by the external bias of tunable AIs; the Darlington configuration of composite transistors provides high gain; furthermore, the adoption of the small size AIs instead of large size passive spiral inductor, and the use of composite resistors make the LNA have a small size. The LNA is fabricated and verified by GaAs/InGaP hetero-junction bipolar transistor (HBT) process. The results show that at the frequency of 7GHz, the gain S21 is maximum and up to 19dB; the S21 can be tuned from 17dB to 19dB by tuning external bias of tunable AIs, that is, the tunable amount of S21 is 2dB, and similarly at 8GHz; the tunable range of 3dB bandwidth is 1GHz. In addition, the gain S21 flatness is better than 0.4dB under frequency from 3.1GHz to 10.6GHz; the size of the LNA only has 760μm×1260μm (including PADs). Therefore, the proposed strategies in the paper provide a new solution to the design of small size and reconfigurable ultra-wideband (UWB) LNA and can be used further to adjust the variations of gain and bandwidth of radio frequency integrated circuits (RFICs) due to package, parasitic and the variation of fabrication process and temperature. Copyright © by HIGH TECHNOLOGY LETTERS PRESS.
Keyword :
Transistors Transistors Bandwidth Bandwidth Resistors Resistors Composite structures Composite structures Electric inductors Electric inductors Ultra-wideband (UWB) Ultra-wideband (UWB) Impedance matching (electric) Impedance matching (electric) Gallium arsenide Gallium arsenide Feedback amplifiers Feedback amplifiers Low noise amplifiers Low noise amplifiers III-V semiconductors III-V semiconductors
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GB/T 7714 | Zhang, Zheng , Zhang, Yanhua , Yang, Ruizhe et al. A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques [J]. | High Technology Letters , 2021 , 27 (1) : 38-42 . |
MLA | Zhang, Zheng et al. "A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques" . | High Technology Letters 27 . 1 (2021) : 38-42 . |
APA | Zhang, Zheng , Zhang, Yanhua , Yang, Ruizhe , Shen, Pei , Ding, Chunbao , Liu, Yaze et al. A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques . | High Technology Letters , 2021 , 27 (1) , 38-42 . |
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