상세정보
학술지
Optimal Resource Allocation Considering Non-Uniform Spatial Traffic Distribution in Ultra-Dense Networks: A Multi-Agent Reinforcement Learning Approach
- 발행일
- 202202
- 출처
- IEEE Access, v.10, pp.20455-20464
- ISSN
- 2169-3536
- 출판사
- IEEE
- 협약과제
- 21HH1100, [통합과제] 5G NR 기반 지능형 오픈 스몰셀 기술 개발, 나지현
- 초록
- Recently, the demand for small cell base stations (SBSs) has been exploding to accommodate the explosive increase in mobile data traffic. In ultra-dense small cell networks (UDSCNs), because the spatial and temporal traffic distributions are significantly disproportionate, the efficient management of the energy consumption of SBSs is crucial. Therefore, we herein propose a multi-agent distributed Q-learning algorithm that maximizes energy efficiency (EE) while minimizing the number of outage users. Through intensive simulations, we demonstrate that the proposed algorithm outperforms conventional algorithms in terms of EE and the number of outage users. Even though the proposed reinforcement learning algorithm has significantly lower computational complexity than the centralized approach, it is shown that it can converge to the optimal solution.
- KSP 제안 키워드
- Centralized approach, Distributed Q-learning, Energy Efficiency, Learning approach, Lower computational complexity, Mobile data traffic, Non-uniform, Optimal Solution, Optimal resource allocation, Q-learning Algorithm, Reinforcement Learning(RL)
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