標(biāo)題: Titlebook: Broadband Communications, Computing, and Control for Ubiquitous Intelligence; Lin Cai,Brian L. Mark,Jianping Pan Book 2022 The Editor(s) ( [打印本頁] 作者: 注射 時(shí)間: 2025-3-21 17:53
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書目名稱Broadband Communications, Computing, and Control for Ubiquitous Intelligence讀者反饋學(xué)科排名
作者: Delude 時(shí)間: 2025-3-21 23:46 作者: 民間傳說 時(shí)間: 2025-3-22 02:51
Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement LearningV lineup and user distribution. We target an optimal UAV control policy which is capable of identifying the upcoming change in the UAV lineup (quit or join-in) or user distribution, and proactively relocating the UAVs ahead of the change rather than passively dispatching the UAVs after the change. S作者: 致敬 時(shí)間: 2025-3-22 08:35 作者: Decline 時(shí)間: 2025-3-22 09:36
Intelligentized Radio Access Network for Joint Optimization of User Association and Power AllocationMulti-Point Joint Transmission (CoMP-JT) technique is proposed to solve the problem, which could transfer interfering signals into useful signals. As CoMP-JT employed, data for a UE is jointly transmitted from multiple TRPs in the same cooperating cluster, thus improve the UEs’ data rate. In this ch作者: Insufficient 時(shí)間: 2025-3-22 14:56 作者: 粗糙 時(shí)間: 2025-3-22 20:46 作者: condescend 時(shí)間: 2025-3-22 22:36
State Transition Field: A New Framework for Mobile Dynamic Cachingheory. The STF characterizes the dynamic change of the cache state distribution in the vector space as a result of content requests and replacements. We consider the case of time-invariant content popularity first and show that the STF can be used to analyze replacement schemes. Then, in the case of作者: 占線 時(shí)間: 2025-3-23 02:32 作者: 好色 時(shí)間: 2025-3-23 08:29
Mobile Computation Offloading with Hard Task Completion Timesnes are difficult to meet in conventional computation offloading due to the stochastic nature of the wireless channels involved. By sometimes allowing simultaneous local and remote execution, job deadlines are always satisfied in the face of any unexpected wireless channel conditions. Online optimal作者: 不透明 時(shí)間: 2025-3-23 12:48 作者: Calculus 時(shí)間: 2025-3-23 17:22
Collaborative Deep Neural Network Inference via Mobile Edge Computingices and the network edge is a potential solution to support DNN inference. Moreover, the sampling rates of mobile devices can be dynamically configured to adapt to network conditions, which can be used to minimize the inference service delay. In this chapter, we first introduce the concept of DNN i作者: 禁止,切斷 時(shí)間: 2025-3-23 20:06
Automated Data-Driven System for Compliance Monitoringarmful interference and improving the overall quality of spectrum. It protects the integrity of spectrum and radio environments, which in turn enables orderly implementation of related management activities such as spectrum engineering, planning, and licensing activities. This chapter presents an au作者: 新娘 時(shí)間: 2025-3-24 00:42 作者: enhance 時(shí)間: 2025-3-24 04:09
Control and Communication Coordination for Industrial Digital Twins of Sintering Processitor and manage all factors in the industrial process based on virtual representations, greatly improve the production quality control of manufacturing. Its popularization demands strong supports of the industrial field network and coordination of control and communication. This chapter proposes a n作者: glowing 時(shí)間: 2025-3-24 08:07
Broadband Communications, Computing, and Control for Ubiquitous Intelligence作者: discord 時(shí)間: 2025-3-24 10:42
2366-1186 nications, computing, and control;.Includes a review of 5G/6G communication technologies, network protocol and architecture design, and ubiquitous computing..978-3-030-98066-5978-3-030-98064-1Series ISSN 2366-1186 Series E-ISSN 2366-1445 作者: 神刊 時(shí)間: 2025-3-24 17:47
Book 2022elligence;.Presents a novel paradigm of ubiquitous intelligence powered by broadband communications, computing, and control;.Includes a review of 5G/6G communication technologies, network protocol and architecture design, and ubiquitous computing..作者: 燒瓶 時(shí)間: 2025-3-24 19:45
https://doi.org/10.1007/978-3-322-82536-0tial networking paradigm shift with new service key performance indicators (KPIs). To satisfy B5G service requirements, a framework of AI-assisted network slicing lifecycle is developed to automate the slice creation with reduced slice management complexity. A case study is presented to demonstrate 作者: Cytokines 時(shí)間: 2025-3-25 01:11
https://doi.org/10.1007/978-3-658-14517-0and action space, deep deterministic policy gradient (DDPG) algorithm, which is an actor-critic based DRL method, is exploited. Furthermore, to promote learning exploration around the timing of the change, the original DDPG scheme is adapted into an asynchronous parallel computing (APC) structure wh作者: Ingrained 時(shí)間: 2025-3-25 05:44 作者: 要素 時(shí)間: 2025-3-25 09:05 作者: PLIC 時(shí)間: 2025-3-25 13:15
https://doi.org/10.1007/978-3-8349-9289-5d. Then, a centralized routing scheme with mobility prediction (CRS-MP) for IoV assisted by a software-defined network (SDN) controller powered with artificial intelligence is introduced. Specifically, through advanced artificial neural network (ANN) technique, the SDN controller is able to perform 作者: oracle 時(shí)間: 2025-3-25 17:47
,Grundzüge des K?uferverhaltens,blem for minimizing the task delay and the ratio of dropped tasks. We propose a deep Q-learning-based algorithm that enables the mobile devices to make their task offloading decisions in a decentralized fashion with local information. This algorithm incorporates double deep Q-network (DQN) and dueli作者: 流動(dòng)才波動(dòng) 時(shí)間: 2025-3-25 23:11
,Grundzüge des K?uferverhaltens,n number of parts, each part is uploaded separately, and a MultiOpt algorithm is used to decide the upload initiation time of each part during runtime. The problem is studied for homogeneous Markovian wireless channels, and energy optimality is proved for the OnOpt algorithm based on continuous uplo作者: 富饒 時(shí)間: 2025-3-26 02:19 作者: 最小 時(shí)間: 2025-3-26 06:32 作者: 不舒服 時(shí)間: 2025-3-26 09:14 作者: 變異 時(shí)間: 2025-3-26 13:15 作者: Lasting 時(shí)間: 2025-3-26 20:24
Utility-Based Dynamic Resource Allocation in IEEE 802.11ax Networks: A Genetic Algorithm Approachels should be allocated to which stations. Furthermore, we adopt a genetic algorithm to solve the derived optimization problem since the problem is known to be NP-hard. We map the proposed scheme, UDRA, onto a terminology of genetic algorithm. Extensive simulation results demonstrate that UDRA can g作者: 知識(shí) 時(shí)間: 2025-3-26 22:57 作者: 摻和 時(shí)間: 2025-3-27 01:56 作者: Mortal 時(shí)間: 2025-3-27 07:27
Deep Reinforcement Learning for Mobile Edge Computing Systemsblem for minimizing the task delay and the ratio of dropped tasks. We propose a deep Q-learning-based algorithm that enables the mobile devices to make their task offloading decisions in a decentralized fashion with local information. This algorithm incorporates double deep Q-network (DQN) and dueli作者: Facet-Joints 時(shí)間: 2025-3-27 12:59
Mobile Computation Offloading with Hard Task Completion Timesn number of parts, each part is uploaded separately, and a MultiOpt algorithm is used to decide the upload initiation time of each part during runtime. The problem is studied for homogeneous Markovian wireless channels, and energy optimality is proved for the OnOpt algorithm based on continuous uplo作者: HERTZ 時(shí)間: 2025-3-27 14:08 作者: palliative-care 時(shí)間: 2025-3-27 21:47 作者: 組成 時(shí)間: 2025-3-27 23:05
https://doi.org/10.1007/978-3-658-35823-5 “official retirement date” was in 2001. Since he joined the Department of Electrical and Computer Engineering at the University of Waterloo in 1970 and served as its chair from 1984–1990, Prof. Mark has helped and influenced thousands of students and colleagues, and contributed to the growing reput作者: 耕種 時(shí)間: 2025-3-28 02:26 作者: 加花粗鄙人 時(shí)間: 2025-3-28 08:47 作者: 欺騙世家 時(shí)間: 2025-3-28 13:20 作者: 葡萄糖 時(shí)間: 2025-3-28 18:22
,Grundzüge des K?uferverhaltens,Multi-Point Joint Transmission (CoMP-JT) technique is proposed to solve the problem, which could transfer interfering signals into useful signals. As CoMP-JT employed, data for a UE is jointly transmitted from multiple TRPs in the same cooperating cluster, thus improve the UEs’ data rate. In this ch作者: Congruous 時(shí)間: 2025-3-28 19:06 作者: delusion 時(shí)間: 2025-3-29 01:43 作者: 整頓 時(shí)間: 2025-3-29 05:15
,Grundzüge des K?uferverhaltens,heory. The STF characterizes the dynamic change of the cache state distribution in the vector space as a result of content requests and replacements. We consider the case of time-invariant content popularity first and show that the STF can be used to analyze replacement schemes. Then, in the case of作者: 誘拐 時(shí)間: 2025-3-29 07:13
,Grundzüge des K?uferverhaltens,l resources. Such decision making can be challenging because the environment in MEC systems can be complex and involve time-varying system dynamics. To address such challenges, deep reinforcement learning (DRL) emerges as a promising method. It enables agents (e.g., network entities, mobile devices)作者: refine 時(shí)間: 2025-3-29 13:21 作者: 散布 時(shí)間: 2025-3-29 16:42 作者: Jocose 時(shí)間: 2025-3-29 21:20
https://doi.org/10.1007/978-3-8349-9289-5ices and the network edge is a potential solution to support DNN inference. Moreover, the sampling rates of mobile devices can be dynamically configured to adapt to network conditions, which can be used to minimize the inference service delay. In this chapter, we first introduce the concept of DNN i作者: 罐里有戒指 時(shí)間: 2025-3-30 00:45
https://doi.org/10.1007/978-3-8349-9289-5armful interference and improving the overall quality of spectrum. It protects the integrity of spectrum and radio environments, which in turn enables orderly implementation of related management activities such as spectrum engineering, planning, and licensing activities. This chapter presents an au作者: 裁決 時(shí)間: 2025-3-30 06:03 作者: 正面 時(shí)間: 2025-3-30 10:01 作者: Cholagogue 時(shí)間: 2025-3-30 12:38 作者: institute 時(shí)間: 2025-3-30 19:32
Lin Cai,Brian L. Mark,Jianping PanProvides comprehensive coverage of enabling communications, computing, and control technologies.Presents a novel paradigm of ubiquitous intelligence powered by broadband communications,.Includes a rev作者: ligature 時(shí)間: 2025-3-31 00:14
Wireless Networkshttp://image.papertrans.cn/b/image/191226.jpg作者: daredevil 時(shí)間: 2025-3-31 01:35 作者: Cpap155 時(shí)間: 2025-3-31 07:03
978-3-030-98066-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 斜 時(shí)間: 2025-3-31 11:50 作者: MILK 時(shí)間: 2025-3-31 17:03
