DGIST Scholar: Revealing Insights for Small Cell Networks via Optimization and Deep Reinforcement Learning

Department of Electrical Engineering and Computer Science Theses Master

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Revealing Insights for Small Cell Networks via Optimization and Deep Reinforcement Learning

Title: Revealing Insights for Small Cell Networks via Optimization and Deep Reinforcement Learning

Alternative Title: 스몰 셀 네트워크에서 효용 함수 최대화를 위한 최적화 및 심층 강화학습 기반 네트워크 자원 관리 알고리즘 개발

Table Of Contents: 1. Introduction
1.1 Expected Future Network and Challenges
1.2 Motivation and Contributions
2. ULTIMA: Ultimate Balance of Centralized and Distributed Benefits for Interference Management in 5G Cellular Networks
2.1 Introduction
2.2. Related Works
2.3 EdgeSON architecture
2.4 System Model
2.4.1 Resource and Allocation Model
2.4.2 Link Model
2.5 Multi-tier IMPowerShare Algorithm
2.5.1 Problem Formulation
2.5.2 Algorithm Development
2.5.3 Algorithm Description
2.6. Performance Analysis
2.6.1 Simulation Results
2.7 Discussion
2.8 Conclusion
2.9 Appendix
2.9.1 Proof of Lemma 1.
3. Judgement-based Deep Q-Learning Framework for Interference Management in Small Cell Networks
3.1 Introduction
3.2 System Model and Problem Formulation
3.3 Multi-Agent DQN Approach with Novel Architecture and Reward Design
3.3.1 Deep Q-Network Architecture Design
3.3.2 State, Action and Reward Design
3.4 Simulation Results and Analysis
3.5 Conclusion

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