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Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks

Sangwon Hwang, Hanjin Kim, Hoon Lee, Inkyu Lee
2020 IEEE Transactions on Vehicular Technology  
This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple hybrid access points (  ...  We design a distributed reinforcement learning strategy where H-APs individually determine time and power allocation variables.  ...  This paper investigates a deep reinforcement learning (DRL) approach for the WPCN which allows distributed calculations at individual H-APs.  ... 
doi:10.1109/tvt.2020.3029609 fatcat:z2tnborhnzel7oe5bcbmeinkz4

2020 Index IEEE Transactions on Wireless Communications Vol. 19

2020 IEEE Transactions on Wireless Communications  
., Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach; TWC June 2020 3908-3923 Hu, Y.H., see Xia, M., TWC June 2020 3769-3781 Hua, C., see Li, M  ...  for MIMO Systems in Dynamic Environments; TWC June 2020 3643-3657 Huang, C., see Yang, M., TWC Sept. 2020 5860-5874 Huang, D., Tao, X., Jiang, C., Cui, S., and Lu, J  ...  ., +, TWC Oct. 2020 6520-6534 Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks.  ... 
doi:10.1109/twc.2020.3044507 fatcat:ie4rwz4dgvaqbaxf3idysubc54

Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions

Ximing Wang, Yuhua Xu, Chaohui Chen, Xiaoqin Yang, Jiaxin Chen, Lang Ruan, Yifan Xu, Runfeng Chen
2020 IEEE Access  
However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources.  ...  INDEX TERMS Unmanned swarm system, spectrum sharing, machine learning, multi-agent learning, game theory.  ...  MULTI-AGENT LEARNING FRAMEWORK FOR INTELLIGENT UNMANNED SWARM COMMUNICATION SYSTEMS In Fig. 3 , a multi-agent learning framework for a USCS is presented.  ... 
doi:10.1109/access.2020.2994198 fatcat:zb6b4ma6cvbhfch2we7sdm4ybu

2021 Index IEEE Transactions on Vehicular Technology Vol. 70

2021 IEEE Transactions on Vehicular Technology  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  -that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  ., +, TVT April 2021 3479-3492 Graph Attention Network-Based Multi-Agent Reinforcement Learning for Slicing Resource Management in Dense Cellular Network.  ... 
doi:10.1109/tvt.2022.3151213 fatcat:vzuzqu54irebpibzp3ykgy5nca

2021 Index IEEE Transactions on Wireless Communications Vol. 20

2021 IEEE Transactions on Wireless Communications  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  -that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  ., +, TWC Feb. 2021 1363-1378 Deep Reinforcement Learning for Multi-Agent Power Control in Heterogeneous Networks.  ... 
doi:10.1109/twc.2021.3135649 fatcat:bgd3vzb7pbee7jp75dnbucihmq

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning [article]

Xiangwei Zhou, Mingxuan Sun, Geoffrey Ye Li, Biing-Hwang Juang
2018 arXiv   pre-print
The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems.  ...  We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications  ...  A distributed multi-agent multi-band reinforcement learning framework is developed in [153] for spectrum sensing in ad hoc cognitive radio networks.  ... 
arXiv:1710.11240v4 fatcat:elt77cgcxvappbxvspp7evb74u

Game Theory and Machine Learning in UAVs-Assisted Wireless Communication Networks: A Survey [article]

M. Zhou, Y. Guan, M. Hayajneh, K. Niu, C. Abdallah
2021 arXiv   pre-print
With increased applications comes the increased demand for advanced algorithms for resource allocation and energy management.  ...  Communication Networks (U-WCNs).  ...  [110] proposed a multi-agent deep Q-learning method for multi-UAV trajectory design in a cellular Internet of UAVs.  ... 
arXiv:2108.03495v1 fatcat:g2gd64ugobalxmw5j74v7auaby

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Niyato 4924 Joint Network Control and Resource Allocation for Space-Terrestrial Integrated Network Through Hierarchal Deep Actor-Critic Reinforcement Learning . . . . . . . . . . . . . . . . . . . . .  ...  Misra 4993 Sustainable Task Offloading in UAV Networks via Multi-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3080800 fatcat:kek3g7wqlzeufhxs26h4qxe5fm

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [article]

Emna Baccour, Naram Mhaisen, Alaa Awad Abdellatif, Aiman Erbad, Amr Mohamed, Mounir Hamdi, Mohsen Guizani
2021 arXiv   pre-print
Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination  ...  We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system.  ...  Particularly, we present the resource management for federated learning, multi-agent reinforcement learning, and active learning.  ... 
arXiv:2105.01798v1 fatcat:4tnq2wjw4bcqdfvhnoij55s2rm

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges [article]

Lei Lei, Yue Tan, Kan Zheng, Shiwen Liu, Kuan Zhang, Xuemin Shen
2020 arXiv   pre-print
In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement  ...  learning (DRL) for decision making.  ...  Deep Distributed Recurrent Q-Networks (DDRQN) [55] . 2) Multi-Agent DRL: In the previous sections, we mainly discuss the DRL methods for single-agent cases.  ... 
arXiv:1907.09059v3 fatcat:z7yksnu4wve7norrjijnu43kvi

AI-Assisted Low Information Latency Wireless Networking [article]

Zhiyuan Jiang, Siyu Fu, Sheng Zhou, Zhisheng Niu, Shunqing Zhang and Shugong Xu
2019 arXiv   pre-print
An AI-assisted Situationally-aware Multi-Agent Reinforcement learning framework for wireless neTworks (SMART) is presented to address the information latency optimization challenge.  ...  conventional communication latency of uRLLC in wireless networked control systems.  ...  SMART: Situationally-Aware Multi-Agent Reinforcement Learning Framework for Wireless Networks To realize self-optimized, adaptive and distributed situational-awareness for information latency optimization  ... 
arXiv:1912.01319v1 fatcat:5fnhq2wuznfrbk7brat53zdspi

2020 Index IEEE Transactions on Vehicular Technology Vol. 69

2020 IEEE Transactions on Vehicular Technology  
Wireless Powered Spatial Crowdsourcing Networks; TVT Jan. 2020 920-934 Jibrin, R., see Jia, Y., TVT Dec. 2020 14173-14187 Jin, B., see Zhu, Y., TVT Aug. 2020 8317-8328 Jin, D., see  ...  see Gholami, R., TVT Sept. 2020 9938-9950 Hoki, K., see Kawakami, T., TVT Dec. 2020 16168-16172 Hong, C., Shan, H., Song, M., Zhuang, W., Xiang, Z., Wu, Y., and Yu, X., A Joint Design of Platoon Communication  ...  ., +, TVT Feb. 2020 1828-1840 Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks.  ... 
doi:10.1109/tvt.2021.3055470 fatcat:536l4pgnufhixneoa3a3dibdma

Access and Radio Resource Management for IAB Networks Using Deep Reinforcement Learning

Malcolm M. Sande, Mduduzi C. Hlophe, Bodhaswar T. Maharaj
2021 IEEE Access  
This article proposes a radio resource management solution for congestion avoidance on the access side of an integrated access and backhaul (IAB) network using deep reinforcement learning (DRL).  ...  Here, the resource management problem was converted into a constrained problem using Markov decision processes and dynamic power management, where a deep neural network was trained for optimal power allocation  ...  In this work, we aim to improve resource management and user satisfaction through congestion avoidance by applying deep reinforcement learning in mm-wave IAB networks. A.  ... 
doi:10.1109/access.2021.3104322 fatcat:of7ou4cqerbd5pdk7vpbfxtq6e

Intelligent Reflective Surface Deployment in 6G: A Comprehensive Survey [article]

Faisal Naeem, Georges Kaddoum, Saud Khan, Komal S. Khan
2022 arXiv   pre-print
Furthermore, we survey model-free reinforcement learning (RL) techniques from the deployment aspect to address the challenges of achieving higher capacity in complex and mobile IRS-assisted UAV wireless  ...  This paper sheds light on the different deployment strategies for IRSs in future terrestrial and non-terrestrial networks.  ...  A distributed multi-agent reinforcement learning scheme is developed for the two cooperative sub-tasks; each relay node represents an agent in the distributed learning.  ... 
arXiv:2204.01152v1 fatcat:sjy7hkp3bbcsxos7rfijggwbkm

Energy Management for Internet of Things via Distributed Systems

Mohammed Mohammed sadeeq, Subhi Zeebaree
2021 Journal of Applied Science and Technology Trends  
For the grid to use unreliable electricity sources, the end-user's on-demand presence in the intelligent energy management context is essential.  ...  In the implementation of aggregators for energy management systems, the objective is to understand the patterns, threats, obstacles and potential obstacles.  ...  ., [96] residential Q-learning, Multi- Agents System, Explored a multi- agent reinforcement approach to energy conservation in residential areas.  ... 
doi:10.38094/jastt20285 fatcat:qlenqhf3pbfs3b2ibwxos4qawm
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