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arXiv 2024年

作者： Opie, Simon Verscharen, Daniel Chen, Christopher H.K. Owen, Christopher J. Isenberg, Philip A. Sorriso-Valvo, Luca Franci, Luca Matteini, Lorenzo Mullard Space Science Laboratory University College London DorkingRH5 6NT United Kingdom Department of Physics and Astronomy Queen Mary University of London LondonE1 4NS United Kingdom Space Science Center University of New Hampshire DurhamNH03824 United States CNR Istituto per la Scienza e la Tecnologia dei Plasmi via Amendola 122/D Bari70126 Italy Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Teknikringen 31 Stockholm11428 Sweden Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom The Blackett Laboratory Department of Physics Imperial College London LondonSW7 2AZ United Kingdom

Where and under what conditions the transfer of energy between electromagnetic fields and particles takes place in the solar wind remains an open question. We investigate the conditions that promote the growth of kinetic instabilities predicted by linear theory, to infer how turbulence and temperature-anisotropy-driven instabilities are interrelated. Using a large dataset from Solar Orbiter, we introduce the radial rate of strain, a novel measure computed from single-spacecraft data, that we interpret as a proxy for the double-adiabatic strain rate. The solar wind exhibits high absolute values of the radial rate of strain at locations with large temperature anisotropy. We measure the kurtosis and skewness of the radial rate of strain from the statistical moments to show that it is non-Gaussian for unstable intervals and increasingly intermittent at smaller scales with a power-law scaling. We conclude that the velocity field fluctuations in the solar wind contribute to the presence of temperature anisotropy sufficient to create potentially unstable conditions. © 2024, CC BY.

关键词： Solar wind

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Status and future development of Heating and Current Drive for the EU DEMO

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FUSION engineering AND DESIGN 2022年第0期180卷 1页

作者： Tran, M. Q. Agostinetti, P. Aiello, G. Avramidis, K. Baiocchi, B. Barbisan, M. Bobkov, V. Briefi, S. Bruschi, A. Chavan, R. Chelis, I. Day, Ch. Delogu, R. Ell, B. Fanale, F. Fassina, A. Fantz, U. Faugel, H. Figini, L. Fiorucci, D. Friedl, R. Franke, Th Gantenbein, G. Garavaglia, S. Granucci, G. Hanke, S. Hogge, J. -p. Hopf, C. Kostic, A. Illy, S. Ioannidis, Z. Jelonnek, J. Jin, J. Latsas, G. Louche, F. Maquet, V. Maggiora, R. Messiaen, A. Milanesio, D. Mimo, A. Moro, A. Ochoukov, R. Ongena, J. Pagonakis, I. G. Peponis, D. Pimazzoni, A. Ragona, R. Rispoli, N. Ruess, T. Rzesnicki, T. Scherer, T. Spaeh, P. Starnella, G. Strauss, D. Thumm, M. Tierens, W. Tigelis, I. Tsironis, C. Usoltceva, M. Van Eester, D. Veronese, F. Vincenzi, P. Wagner, F. Wu, C. Zeus, F. Zhang, W. [a]Ecole Polytechnique Fédérale de Lausanne (EPFL) Swiss Plasma Center (SPC) Station 13 Lausanne 1015 Switzerland [b]Consorzio RFX (CNR ENEA INFN Università di Padova Acciaierie Venete SpA Corso Stati Uniti 4 Padova I-35127 Italy [c]IAM-AWP Karlsruhe Institute of Technology (KIT) Kaiserstr. 12 Karlsruhe 76131 Germany [d]IHM Karlsruhe Institute of Technology (KIT) Kaiserstr. 12 Karlsruhe 76131 Germany [e]Institute for Plasma Science and Technology National Research Council (ISTP-CNR) via Cozzi 53 Milan 20125 Italy [f]Max Planck Institute for Plasma Physics Boltzmann Strasse 2 Garching 85748 Germany [g]National and Kapodistrian University of Athens Department of Physics Zografou Athens GR-157 84 Greece [h]ITEP Karlsruhe Institute of Technology (KIT) Campus North Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen 76344 Germany [i]AG Experimentelle Plasmaphysik Universitt Augsburg Augsburg 86135 Germany [j]EUROfusion Consortium Boltzmannstr. 2 Garching 85748 Germany [k]LPP-ERM/KMS Laboratory for Plasma Physics Royal Military Academy TEC Partner Renaissancelaan 30 Brussels 1000 Belgium [l]Politecnico di Torino Corso Duca degli Abruzzi 24 Torino 10129 Italy [m]National Technical University of Athens School of Electrical and Computer Engineering Zografou Athens GR-157 71 Greece [n]Max Planck Institute for Plasma Physics Wendelsteinstrasse 1 Greifswald 17491 Germany

The European DEMO is a pulsed device with pulse length of 2 hours. The functions devoted to the heating and current drive system are: plasma breakdown, plasma ramp-up to the flat-top where fusion reactions occur, the control of the plasma during the flat-top phase, and finally the plasma ramp-down. The EU-DEMO project was in a Pre-Concept Design Phase during 2014-2020, meaning that in some cases, the design values of the device and the precise requirements from the physics point of view were not yet frozen. A total of 130 MW was considered for the all phases of the plasma: in the flat top, 30 MW is required for neoclassical tearing modes (NTM) control, 30 MW for burn control, and 70 MW for the control of thermal instability (TI), without any specific functions requested from each system, Electron Cyclotron (EC), Ion Cyclotron (IC), or Neutral Beam (NB) Injection. At the beginning of 2020, a strategic decision was taken, to consider EC as the baseline for the next phase (in 2021 and beyond). R&D on IC and NB will be risk mitigation measures. In parallel with progresses in Physics modelling, a decision point on the heating strategy will be taken by 2024. This paper describes the status of the R&D development during the period 2014-2020. It assumes that the 3 systems EC, IC and NB will be needed. For integration studies, they are assumed to be implemented at a power level of at least 50 MW. This paper describes in detail the status reached by the EC, IC and NB at the end of 2020. It will be used in the future for further development of the baseline heating method EC, and serves as starting point to further develop IC and NB in areas needed for these systems to be considered for DEMO.

关键词： Heating Current Drive Electron Cyclotron Ion Cyclotron Neutral Beam DEMO

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Three-dimensional modelling of the shock-turbulence interaction

arXiv

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arXiv 2023年

作者： Trotta, Domenico Pezzi, Oreste Burgess, David Preisser, Luis Blanco-Cano, Xochitl Kajdic, Primoz Hietala, Heli Horbury, Timothy S. Vainio, Rami Dresing, Nina Retino', Alessandro Marcucci, Maria Federica Sorriso-Valvo, Luca Servidio, Sergio Valentini, Francesco The Blackett Laboratory Department of Physics Imperial College London LondonSW7 2AZ United Kingdom Consiglio Nazionale delle Ricerche Via Amendola 122/D BariI-70126 Italy Department of Physics and Astronomy Queen Mary University of London E1 4NS United Kingdom Space Research Institute Austrian Academy of Sciences Graz Austria Departamento de Ciencias Espaciales Instituto de Geofísica Universidad Nacional Autónoma de México Ciudad Universitaria Ciudad de México Mexico Department of Physics and Astronomy University of Turku TurkuFI-20014 Finland LPP Ecole Polytechnique CNRS UPMC Université Paris Sud Palaiseau France INAF-Istituto di Astrofisica e Planetologia Spaziali Rome Italy Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Ångström Laboratory Lägerhyddsvägen 1 Uppsala75121 Sweden Dipartimento di Fisica Università della Calabria Rende87036 Italy

The complex interaction between shocks and plasma turbulence is extremely important to address crucial features of energy conversion in a broad range of astrophysical systems. We study the interaction between a supercritical, perpendicular shock and pre-existing, fully-developed plasma turbulence, employing a novel combination of magnetohydrodynamic (MHD) and small-scale, hybrid-kinetic simulations where a shock is propagating through a turbulent medium. The variability of the shock front in the unperturbed case and for two levels of upstream fluctuations is addressed. We find that the behaviour of shock ripples, i.e., shock surface fluctuations with short (a few ion skin depths, ) wavelengths, is modified by the presence of pre-existing turbulence, which also induces strong corrugations of the shock front at larger scales. We link this complex behaviour of the shock front and the shock downstream structuring with the proton temperature anisotropies produced in the shock-turbulence system. Finally, we put our modelling effort in the context of spacecraft observations, elucidating the role of novel cross-scale, multi-spacecraft measurements in resolving shock front irregularities at different scales. These results are relevant for a broad range of astrophysical systems characterised by the presence of shock waves interacting with plasma turbulence. © 2023, CC BY.

关键词： Shock waves

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Proton Imaging of High-Energy-Density laboratory plasmas

arXiv

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arXiv 2022年

作者： Schaeffer, Derek B. Bott, Archie F.A. Borghesi, Marco Flippo, Kirk A. Fox, William Fuchs, Julien Li, Chikang Séguin, Fredrick H. Park, Hye-Sook Tzeferacos, Petros Willingale, Louise Department of Physics and Astronomy University of California - Los Angeles Los AngelesCA90095 United States Department of Physics University of Oxford Parks Road OxfordOX1 3PU United Kingdom School of Mathematics and Physics The Queen’s University Belfast BelfastBT7 1NN United Kingdom Los Alamos National Laboratory Los AlamosNM87544 United States Princeton Plasma Physics Laboratory PrincetonNJ08543 United States Department of Astrophysical Science Princeton University PrincetonNJ08540 United States LULI-CNRS CEA UPMC Univ Paris 06 Sorbonne Université École Polytechnique Institut Polytechnique de Paris Palaiseau France Plasma Science and Fusion Center Massachusetts Institute of Technology CambridgeMA02139 United States Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Physics and Astronomy University of Rochester RochesterNY14627 United States Gérard Mourou Center for Ultrafast Optical Science Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Proton imaging has become a key diagnostic for measuring electromagnetic fields in high-energy-density (HED) laboratory plasmas. Compared to other techniques for diagnosing fields, proton imaging is a non-perturbative measurement that can simultaneously offer high spatial and temporal resolution and the ability to distinguish between electric and magnetic fields. Consequently, proton imaging has been used in a wide range of HED experiments, from inertial confinement fusion to laboratory astrophysics. An overview is provided on the state of the art of proton imaging, including detailed discussion of experimental considerations like proton sources and detectors, the theory of proton-imaging analysis, and a survey of experimental results demonstrating the breadth of applications. Topics at the frontiers of proton imaging development are also described, along with an outlook on the future of the field. © 2022, CC BY.

关键词： Electromagnetic fields

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二维材料最新研究进展

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物理化学学报 2021年第12期37卷 1-151页

作者：常诚陈伟陈也陈永华陈雨丁峰樊春海范红金范战西龚成宫勇吉何其远洪勋胡晟胡伟达黄维黄元季威李德慧李连忠李强林立凌崇益刘鸣华刘楠刘庄 Kian Ping Loh 马建民缪峰彭海琳邵明飞宋礼苏邵孙硕谭超良唐智勇王定胜王欢王金兰王欣王欣然 Andrew T.S.Wee 魏钟鸣吴宇恩吴忠帅熊杰熊启华徐伟高尹鹏曾海波曾志远翟天佑张晗张辉张其春张铁锐张翔赵立东赵美廷赵伟杰赵运宣周凯歌周兴周喻朱宏伟张华刘忠范 Institute of Science and Technology Austria Am Campus 13400 KlosterneuburgAustria Department of Chemistry National University of Singapore3 Science Drive 3117543Singapore Department of Chemistry The Chinese University of Hong KongShatinNew TerritoriesHong KongChina Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech UniversityNanjing 211816China School of Life Sciences Shanghai UniversityShanghai 200444China Centre for Multidimensional Carbon Materials Institute for Basic ScienceUlsan 44919Korea School of Chemistry and Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Department of Chemistry City University of Hong KongKowloonHong KongChina Department of Electrical and Computer Engineering and Quantum Technology Center University of MarylandCollege ParkMaryland 20742USA School of Materials Science and Engineering Beihang UniversityBeijing 100191China Department of Materials Science and Engineering City University of Hong KongKowloonHong KongChina Center of Advanced Nanocatalysis(CAN) Hefei National Laboratory for Physical Sciences at the MicroscaleDepartment of Applied ChemistryUniversity of Science and Technology of ChinaHefei 230026China College of Chemistry and Chemical Engineering State Key Laboratory of Physical Chemistry of Solid SurfacesCollaborative Innovation Center of Chemistry for Energy Materials(iChEM)Xiamen UniversityXiamen361005Fujian ProvinceChina State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of PhysicsRenmin University of ChinaBeijing 100872China School of Optical and Electronic Information Wuhan National Laboratory

Research on two-dimensional(2D) materials has been explosively increasing in last seventeen years in varying subjects including condensed matter physics, electronic engineering, materials science, and chemistry since the mechanical exfoliation of graphene in 2004. Starting from graphene, 2D materials now have become a big family with numerous members and diverse categories. The unique structural features and physicochemical properties of 2D materials make them one class of the most appealing candidates for a wide range of potential applications. In particular, we have seen some major breakthroughs made in the field of 2D materials in last five years not only in developing novel synthetic methods and exploring new structures/properties but also in identifying innovative applications and pushing forward commercialisation. In this review, we provide a critical summary on the recent progress made in the field of 2D materials with a particular focus on last five years. After a brief backgroundintroduction, we first discuss the major synthetic methods for 2D materials, including the mechanical exfoliation, liquid exfoliation, vapor phase deposition, and wet-chemical synthesis as well as phase engineering of 2D materials belonging to the field of phase engineering of nanomaterials(PEN). We then introduce the superconducting/optical/magnetic properties and chirality of 2D materials along with newly emerging magic angle 2D superlattices. Following that, the promising applications of 2D materials in electronics, optoelectronics, catalysis, energy storage, solar cells, biomedicine, sensors, environments, etc. are described sequentially. Thereafter, we present the theoretic calculations and simulations of 2D materials. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future outlooks in this rapidly developing field.

关键词： Two-dimensional materials Transition metal dichalcogenides Phase engineering of nanomaterials Electronics Optoelectronics Catalysis Energy storage and conversion

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Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

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arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Research Square

引用

Research Square 2021年

作者： Chu, Yanyi Zhang, Yan Wang, Qiankun Zhang, Lingfeng Wang, Xuhong Wang, Yanjing Wang, Jianmin Jiang, Xue Salahub, Dennis Russell Xiong, Yi Wei, Dong-Qing State Key Laboratory of Microbial Metabolism Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances Joint International Research Laboratory of Metabolic & Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200030 China Department of Chemistry CMS - Centre for Molecular Simulation IQST - Institute for Quantum Science and Technology University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Clinical Oncology The University of Hong Kong-Shenzhen Hospital Shenzhen China School of Electrical Engineering and Computer Science University of Ottawa 75 Laurier Ave. OttawaONK1N 6N5 Canada The Ministry of Education Key Laboratory of System control and information processing Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200030 China Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Peng Cheng Laboratory Vanke Cloud City Phase I Building 8 Xili Street Nanshan District Guangdong Shenzhen518055 China

Computational prediction of the interaction between human leukocyte antigen (HLA) and peptide (pHLA) can speed up epitope screening and vaccine design. Here, we develop the TransMut framework composed of TransPHLA for pHLA binding prediction and AOMP for mutated peptide optimization, which can be generalized to any binding and mutation task of biomolecules. Firstly, TransPHLA is developed by using a Transformer-derived self-attention model to predict pHLA binding, which is significantly superior to 11 previous methods on pHLA binding prediction, neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed to automatically optimize mutated peptides to search for mutant peptides with higher affinity to the target HLA and with high homology to the source peptide. Among 3660 non-binding pHLAs, 3630 were successfully mutated. Of these, 94% were verified by the IEDB recommended method, and 88% have homology higher than 80% to the source peptide. © 2021, CC BY.

关键词： Peptides

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

引用

arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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