Load balancing for ultradense networks: A deep reinforcement learning-based approach

Y Xu, W Xu, Z Wang, J Lin, S Cui - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
In this article, we propose a deep reinforcement learning (DRL)-based mobility load
balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load …
被引用次数：100 相关文章

Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach

Y Xu, W Xu, Z Wang, J Lin, S Cui - arXiv e-prints, 2019 - ui.adsabs.harvard.edu
In this paper, we propose a deep reinforcement learning (DRL) based mobility load
balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load …
[PDF] ieee.org

[PDF][PDF] Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach

Y Xu, W Xu, Z Wang, J Lin, S Cui - ieeexplore.ieee.org
In this paper, we propose a deep reinforcement learning (DRL) based mobility load
balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load …

Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach

Y Xu, W Xu, Z Wang, J Lin, S Cui - arXiv preprint arXiv:1906.00767, 2019 - arxiv.org
In this paper, we propose a deep reinforcement learning (DRL) based mobility load
balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load …

Load Balancing for Ultradense Networks: A Deep Reinforcement Learning-Based Approach

Y Xu, W Xu, Z Wang, J Lin, S Cui - 2019 - irepository.cuhk.edu.cn
[1] C. Pan, M. Elkashlan, J. Wang, J. Yuan, and L. Hanzo," Usercentric C-RAN architecture
for ultra-dense 5G networks: Challenges and methodologies," IEEE Commun. Mag., vol. 56 …