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Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches
[article]
2019
arXiv
pre-print
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In NOMA, a combination of multiple messages are sent to users, and the defined objective is approached by finding an appropriate power allocation for message signals. ...
The second one is based on the deep reinforcement learning method that allows all users to share the full bandwidth. ...
Therefore, power allocation schemes in non-caching NOMA systems may not be optimal anymore in this context. ...
arXiv:1909.11074v1
fatcat:frxzl3vt3bdbvpaen3x4iufpuq
2020 Index IEEE Transactions on Wireless Communications Vol. 19
2020
IEEE Transactions on Wireless Communications
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., and Saad, W., 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, ...
Identification 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 Jan. 2020 491-506 Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach. ...
doi:10.1109/twc.2020.3044507
fatcat:ie4rwz4dgvaqbaxf3idysubc54
2021 Index IEEE Transactions on Wireless Communications Vol. 20
2021
IEEE Transactions on Wireless Communications
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-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. ...
., +, TWC Feb. 2021 1065-1075 Resource Allocation in Uplink NOMA-IoT Networks: A Reinforce-ment-Learning Approach. ...
doi:10.1109/twc.2021.3135649
fatcat:bgd3vzb7pbee7jp75dnbucihmq
Joint Optimization of Caching and Computation in Multi-Server NOMA-MEC System via Reinforcement Learning
2020
IEEE Access
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In the recent work [15] , the authors proposed the method of deep reinforcement learning to solve power allocation in cache-aided NOMA systems. ...
In practice, when users are agents of deep reinforcement learning, in order to reduce high running delay and energy consumption, there are usually lightweight methods combined with deep reinforcement learning ...
doi:10.1109/access.2020.3002895
fatcat:idehz66d4nflxn5olgjdawpebm
Table of Contents
2021
IEEE Systems Journal
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Savaghebi, and B. Akhbari 2369 Uplink NOMA Using Power Allocation for UAV-Aided CSMA/CA Networks . . . . . . . . . . . Y. Kwon, H. Baek, and J. ...
Chin 2269 Particle Swarm Optimization on Deep Reinforcement Learning for Detecting Social Spam Bots and Spam-Influential Users in Twitter Network . . . . . . . . . . . . . . . . . . . . . . . . . G. ...
doi:10.1109/jsyst.2021.3079624
fatcat:6f2ksqg64zgrrac4iltdf3cmqy
Table of contents
2021
IEEE Transactions on Communications
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Wang and S. Wang 6050 Intelligent Radio Access Network Slicing for Service Provisioning in 6G: A Hierarchical Deep Reinforcement Learning Approach ................................... ...
Tang, and H. Dai 5933 Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach .................................... N. Van Huynh, D. ...
doi:10.1109/tcomm.2021.3107096
fatcat:bpebbvejgfd37gxg4nu4hmkajq
Table of Contents
2021
IEEE Transactions on Vehicular Technology
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Yang 13115 Age-Optimal Information Gathering in Linear Underwater Networks: A Deep Reinforcement Learning Approach . . . . . . ...
Abbas 13149 Semi-Distributed Resource Management in UAV-Aided MEC Systems: A Multi-Agent Federated Reinforcement Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tvt.2021.3128122
fatcat:m76xnufupvde5b73yvrnzf2xlq
2021 Index IEEE Transactions on Vehicular Technology Vol. 70
2021
IEEE Transactions on Vehicular Technology
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-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 Jan. 2021 543-557 Unsupervised Deep Learning Approach for Near Optimal Power Allocation in CRAN. ...
doi:10.1109/tvt.2022.3151213
fatcat:vzuzqu54irebpibzp3ykgy5nca
A New Look at AI-Driven NOMA-F-RANs: Features Extraction, Cooperative Caching, and Cache-Aided Computing
[article]
2021
arXiv
pre-print
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Specifically, we first elaborate on the NOMA-F-RANs architecture, shedding light on the key modules, namely, cooperative caching and cache-aided mobile edge computing (MEC). ...
, and resource allocation strategies. ...
Q-learning [8] NOMA downlink networks Power allocation Hotbooting Q-learning [9] NOMA downlink networks Channel assignment Attention-based NN enabled DRL [10] Cache-aided NOMA downlink Power allocation ...
arXiv:2112.01325v1
fatcat:rhga6egpsvgutk3pcewqnhbi4u
Table of contents
2021
IEEE Transactions on Wireless Communications
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Abdul Basit, Wen-Qin Wang, Shaddrack Yaw Nusenu, and Samad Wali Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach .............. ...
Wen Cui, Chen Liu, Wenjun Yang, and Lin Cai 4269 A Hybrid DQN and Optimization Approach for Strategy and Resource Allocation in MEC Networks ................. .......................................... ...
doi:10.1109/twc.2021.3087933
fatcat:3h6tt6tz3ncn3o2m3yg4ohoom4
Table of Contents
2020
IEEE Transactions on Vehicular Technology
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Monteiro 13112 Joint Optimization of Handover Control and Power Allocation Based on Multi-Agent Deep Reinforcement Learning .
13424 Performance Analysis of Cellular Downlink With Fluctuating Two-Ray ...
Wang 13756 Deep Reinforcement Learning-Based Resource Allocation and Power Control in Small Cells With Limited Information Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tvt.2020.3031771
fatcat:fnby2geqgzggrenxk5blu6sn5y
2020 Index IEEE Transactions on Vehicular Technology Vol. 69
2020
IEEE Transactions on Vehicular Technology
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Revocable Data-Sharing Scheme in VANETs; TVT Dec. 2020 15933-15946 Hoseini, S.A., Ding, M., Hassan, M., and Chen, Y., Analyzing the Impact of Molecular Re-Radiation on the MIMO Capacity in High-Frequency ...
Relaying System With SWIPT Under Outdated CSI; TVT Dec. 2020 15580-15593 Hodtani, G.A., see Gholami, R., TVT Sept. 2020 9938-9950 Hoki, K., see Kawakami, T., TVT Dec. 2020 16168-16172 Hong, C., ...
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks. ...
doi:10.1109/tvt.2021.3055470
fatcat:536l4pgnufhixneoa3a3dibdma
Table of Contents
2021
IEEE Transactions on Vehicular Technology
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Yang 3972 Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Lee 3984 Deep Learning-Aided Distributed Transmit Power Control for Underlay Cognitive Radio Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tvt.2021.3072527
fatcat:i33ar374abcolbliyz7xrwh4k4
Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends
2021
Electronics
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Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. ...
In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. ...
Paper ML Approach Application Problem Description [68,69] K-means Power allocation Addresses user selection issue and achieves power optimization in NOMA systems [70] PC algorithms D2D systems optimization ...
doi:10.3390/electronics10222786
fatcat:6umid7qnabdttkjyhglpxjpwpm
Guest Editorial: Introduction to the Special Section on Heterogeneous Communications Networks
2020
IEEE Transactions on Network Science and Engineering
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His research interests include application of game theory, optimization, and statistical theories to communication, networking, and resource allocation problems, in particular space networks and heterogeneous ...
His research interests include wireless networks, cloud computing, and cyberphysical systems. ...
In "Deep Learning Based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation," Zhang et al. design a deep learning based framework for radio resource management ...
doi:10.1109/tnse.2020.3026566
fatcat:rej32vuse5bgrajuih26pct3oy
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