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DDPG-based Resource Management for MEC/UAV-Assisted Vehicular Networks [article]

Haixia Peng, Xuemin Shen
2020 arXiv   pre-print
In this paper, we investigate joint vehicle association and multi-dimensional resource management in a vehicular network assisted by multi-access edge computing (MEC) and unmanned aerial vehicle (UAV).  ...  To efficiently manage the available spectrum, computing, and caching resources for the MEC-mounted base station and UAVs, a resource optimization problem is formulated and carried out at a central controller  ...  CONCLUSION This paper has investigated the multi-dimensional resource management problem in the MEC/UAV-assisted vehicular network.  ... 
arXiv:2009.03721v1 fatcat:xtwhyra5cjgbtm3n6c5qx6ksa4

DRL-Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

Hoa TT. Nguyen, Minh T. Nguyen, Hai T. Do, Hoang T. Hua, Cuong V. Nguyen, Stefan Panic
2021 Wireless Communications and Mobile Computing  
A FDRL-based UAV-assisted vehicular communication is discussed to point out the future research directions for the networks.  ...  In addition, a federated deep reinforcement learning- (FDRL-) based vehicular communication is proposed.  ...  Acknowledgments The authors would like to thank the Thai Nguyen University of Technology (TNUT), Ministry of Education and Training (MOET), Viet Nam, for the support.  ... 
doi:10.1155/2021/5051328 fatcat:24mb2beevrbidknjoyk4z7dxze

Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle [article]

Xiaoyu Dai, Kaoru Ota, Mianxiong Dong
2022 arXiv   pre-print
As intelligent transportation systems been implemented broadly and unmanned arial vehicles (UAVs) can assist terrestrial base stations acting as multi-access edge computing (MEC) to provide a better wireless  ...  In the paper, we present a Multi-Agent Graph Convolutional Deep Reinforcement Learning (M-AGCDRL) algorithm which combines local observations of each agent with a low-resolution global map as input to  ...  ACKNOWLEDGMENTS This work is partially supported by JSPS KAKENHI Grant Numbers JP19K20250, JP20F20080, and JP20H04174, Leading Initiative for Excellent Young Researchers (LEADER), MEXT, Japan, and JST,  ... 
arXiv:2202.08972v1 fatcat:qxrhzasghjgp3f3iwra3u5smwm

Guest Editorial: Aerial Computing: Drones for Multi-Access Edge Computing

Jianchao Zheng, Alagan Anpalagan, Mohsen Guizani, Yuan Wu, Ning Zhang, Xianfu Chen, F. Richard Yu
2021 IEEE wireless communications  
The article entitled "Edge Intelligence for Multi-Dimensional Resource Management in Aerial-Assisted Vehicular Networks" by Peng et al. proposes a drone-assisted MEC-enabled vehicular network architecture  ...  UAV-NOMA-MEC framework, where federated learning and reinforcement learning are introduced for intelligent task offloading and computing resource allocation.Agents that learn the strategy from scratch  ... 
doi:10.1109/mwc.2021.9615123 fatcat:6bt4dyze6nf55eb5tbc3skg5ai

Table of contents

2021 IEEE Journal on Selected Areas in Communications  
Multi-Agent Reinforcement Learning Based Resource Management in MEC-and UAV-Assisted Vehicular Networks .................................................................................................  ...  and Networking Series on Machine Learning in Communications and Networks Latest Advances in Optical Networks for 5G Communications and Beyond UAV Communications in 5G and Beyond Networks Distributed Learning  ... 
doi:10.1109/jsac.2020.3040917 fatcat:l7c5xs7xa5c37ori2f4lfbcmra

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Abbas 13149 Semi-Distributed Resource Management in UAV-Aided MEC Systems: A Multi-Agent Federated Reinforcement Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Chatzinotas 12872 Game Combined Multi-Agent Reinforcement Learning Approach for UAV Assisted Offloading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3128122 fatcat:m76xnufupvde5b73yvrnzf2xlq

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Guan 8186 Multi-Agent Reinforcement Learning Based 3D Trajectory Design in Aerial-Terrestrial Wireless Caching Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Fantacci, and Z. Han 8216 FogPrime: Dynamic Pricing-Based Strategic Resource Management in Fog Networks . . . . . . .S. C. Misra and A.  ... 
doi:10.1109/tvt.2021.3100836 fatcat:hpsmhmav2zdh5ggtbe5r4xjoc4

Table of Contents

2020 IEEE Transactions on Vehicular Technology  
Multi-Agent Deep Reinforcement Learning for Urban Traffic Light Control in Vehicular Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Le 8886 Joint Channel Allocation and Resource Management for Stochastic Computation Offloading in MEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2020.3011246 fatcat:jc4kjbytyfgi5h5oe53i4b7ew4

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

Asrar Ahmed Baktayan, Ibrahim Ahmed Al-Baltah, Abdul Azim Abd Ghani
2022 Journal of Communications Software and Systems  
In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number  ...  This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles.  ...  The price offered to vehicles can assist UAV operators in making better use of network resources and managing congestion issues.  ... 
doi:10.24138/jcomss-2021-0154 fatcat:wydd6zf2yrcxzgl7mhqmsk6f44

2021 Index IEEE Transactions on Vehicular Technology Vol. 70

2021 IEEE Transactions on Vehicular Technology  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Note that the item title is found only under the primary entry in the Author Index.  ...  ., +, TVT March 2021 2337-2348 Semi-Distributed Resource Management in UAV-Aided MEC Systems: A Multi-Agent Federated Reinforcement Learning Approach.  ... 
doi:10.1109/tvt.2022.3151213 fatcat:vzuzqu54irebpibzp3ykgy5nca

Table of Contents

2021 IEEE Transactions on Network Science and Engineering  
Alazab 2792 Intelligent Ubiquitous Network Accessibility for Wireless-Powered MEC in UAV-Assisted B5G. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Xiong 3179 Secure Computation Offloading in Blockchain Based IoT Networks With Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tnse.2021.3123860 fatcat:7ruw6beu6rablblt5txlvz7oh4

Table of Contents

2021 2021 IEEE 46th Conference on Local Computer Networks (LCN)  
5G and IoT Era 403 A Vehicle Message Scheduling Scheme for Vehicle Trust Management 407 Distributed Task Migration Optimization in MEC by Deep Reinforcement Learning Strategy 411 Alternative  ...  Multi-Agent Deep Reinforcement Learning for SFC Placement on Multiple Domains 299 Suitability of Graph Representation for BGP Anomaly Detection 305 MPRdeep: Multi-Objective Joint Optimal Node Positioning  ... 
doi:10.1109/lcn52139.2021.9524933 fatcat:bopsc4l2qrc7bobzfyb6343iou

Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence [article]

Peng Wei, Kun Guo, Ye Li, Jue Wang, Wei Feng, Shi Jin, Ning Ge, Ying-Chang Liang
2022 arXiv   pre-print
Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC.  ...  Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond.  ...  , such as UAV-based MEC deployment assisted by path planning [61] - [66] .  ... 
arXiv:2201.11410v4 fatcat:24igkq4kbrb2pjzwf3mf3n7qtq

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Kwon LSTM-Based Channel Access Scheme for Vehicles in Cognitive Vehicular Networks With Multi-Agent Settings . . . . . . .  ...  Ansari Multi-Agent Deep Reinforcement Learning for Computation Offloading and Interference Coordination in Small Cell Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3108593 fatcat:feewwd6epzgjvg7v3g3dpmxopa

A Comprehensive Survey on Aerial Mobile Edge Computing: Challenges, State-of-the-Art, and Future Directions [article]

Zhengyu Song, Xintong Qin, Yuanyuan Hao, Tianwei Hou, Jun Wang, Xin Sun
2022 arXiv   pre-print
allocation, UAV deployment, task scheduling and load balancing, interplay between aerial MEC and other technologies, as well as the machine-learning (ML)-driven optimization.  ...  On the other hand, in order to provide ubiquitous and reliable connectivity in wireless networks, unmanned aerial vehicles (UAVs) can be leveraged as efficient aerial platforms by exploiting their inherent  ...  In the context of MEC-and UAV-assisted vehicular networks, MADDPG is applied in [159] to address the vehicle association and resource allocation problems with continuous action space.  ... 
arXiv:2208.13965v1 fatcat:rglkodmjsve2fbncmmyatqos6y
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