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研究生: Amin Lotfolahi
Amin Lotfolahi
論文名稱: Pioneering Energy Efficiency in Advanced Wireless Networks via Deep Reinforcement Learning: From mmWave HetNets, Industrial IoT to NOMA
Pioneering Energy Efficiency in Advanced Wireless Networks via Deep Reinforcement Learning: From mmWave HetNets, Industrial IoT to NOMA
指導教授: 馮輝文
Huei-Wen Ferng
口試委員: 馮輝文
金台齡
鍾聖倫
陳冠宇
鄭瑞光
蔡志宏
魏宏宇
張宏慶
林嘉慶
學位類別: 博士
Doctor
系所名稱: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年: 2023
畢業學年度: 112
語文別: 英文
論文頁數: 84
外文關鍵詞: Energy Efficiency, Deep Reinforcement Learning (DRL), Heterogeneous Network (HetNet), NonOrthogonal Multiple Access (NOMA), Resource Allocation
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  • Recommendation Letter i Approval Letter ii Abstract in Chinese iii Abstract in English iv Acknowledgements v Contents vi List of Figures ix List of Tables xi List of Algorithms xii 1 Introduction 1 1.1 Overview of Multi-Agent Proximal Policy Optimization (MAPPO) 4 2 Literature Review 9 2.1 BS Activation and Traffic Offloading Solutions in HetNets 9 2.2 Task Offloading and Resource Allocation Solutions in IIoT 10 2.3 Resource Allocation Solutions in NOMA 11 3 A Multi-Agent Proximal Policy Optimized Joint Mechanism in mmWave HetNets with CoMP towards Energy Efficiency Maximization 13 3.1 Introduction 13 3.2 System Model 14 3.2.1 Network Model 14 3.2.2 Mobility Model 15 3.2.3 Communication Model 15 3.2.4 Energy Consumption Model 17 3.3.1 Problem Formulation 17 3.3.2 Clustering-Based User Association Approach 18 3.3.3 MAPPO-Based BS Activation Approach 19 3.4 Simulation Results And Discussions 24 3.4.1 Simulation Setup 24 3.4.2 Results and Discussions 25 3.5 Concluding Remarks 33 4 DRL-Based Resource Allocation in NOMA-Aided Industrial IoT towards Energy Productivity Maximization 34 4.1 Introduction 34 4.2 System Model 35 4.2.1 Network Model 36 4.2.2 Task Models 37 4.2.3 Energy Consumption Model 40 4.3 Proposed Mechanism 41 4.3.1 Energy Productivity 41 4.3.2 Problem Formulation 42 4.3.3 MAPPOBased Joint Task Offloading and Resource Allocation Approach 43 4.4 Simulation Results And Discussions 47 4.5 Concluding Remarks 54 5 A Multi-Agent DRL-Based Power Allocation Mechanism for Multi-Cell NOMA Networks 55 5.1 Introduction 55 5.2 System Model 55 5.3 Proposed Mechanism 57 5.3.1 MADRL-Based Power Allocation Approach 57 5.4 Performance Evaluation and Discussions 60 5.4.1 Simulation Setup 60 5.4.2 Results and Discussions 61 5.5 Concluding Remarks 63 6 Optimizing Power and Subchannel Allocation in an Uplink NOMA System with a Self-Attention DRL-Based Mechanism 64 6.1 Introduction 64 6.2 System Model 65 6.3 Proposed Mechanism 67 6.3.1 Problem Formulation 67 6.3.2 MADRL-Based Joint Power Allocation and Subchannel Allocation Approach 68 6.4 Simulation Results And Discussions 71 6.5 Concluding Remarks 76 7 Conclusions 78 References 79 List of Publications 84

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