Deep Reinforcement Learning for Resource Management in Network Slicing
By Rongpeng Li, Zhifeng Zhao, Qi Sun, Chih-Lin I, Chenyang Yang, Xianfu Chen, Minjian Zhao, Honggang Zhang. IEEE Access, 2018
324 citations recorded by the source corpus.
Network slicing is born as an emerging business to operators by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users’ activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/8fc4a692ee9615d78cff36f35139173e1410399e
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In Artificial Intelligence
In Computer Networks and Communications
- Yaohua Sun
- Mugen Peng
- Shiwen Mao
- H. Vincent Poor
- Yonghui Li
- Branka Vucetic
- Changyang She
- Chengjian Sun
Citations in the corpus
- Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “Recently, authors in [166] and [167] have applied DRL to network”
- Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks (2018, methodology): “Deep reinforcement learning framework has been used in some other wireless communications problems [13]–[16].”
- A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, methodology): “As summarized in Table 8, DRL algorithms have been applied in wireless networks for mobile networking [247]–[249], scheduler design [250]–[254], resource allocation [255], [256], [258], [260]–[262], and routing [263], [264].”