Abstract
| Original language | English |
|---|---|
| Article number | 8540003 |
| Pages (from-to) | 74429 - 74441 |
| Number of pages | 13 |
| Journal | IEEE Access |
| Volume | 6 |
| DOIs | |
| Publication status | Published - 2018 |
| MoE publication type | Not Eligible |
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Keywords
- 5G mobile communication
- Deep Reinforcement Learning
- Network slicing
- Network Slicing
- Neural networks
- Neural Networks
- Q-Learning
- Quality of experience
- Resource management
- Resource Management
Cite this
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Deep Reinforcement Learning for Resource Management in Network Slicing. / Li, Rongpeng; Zhao, Zhifeng; Sun, Qi; Chi-Lin, I.; Yang, Chenyang; Chen, Xianfu; Zhao, Minjian; Zhang, Honggang.
In: IEEE Access, Vol. 6, 8540003, 2018, p. 74429 - 74441.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Deep Reinforcement Learning for Resource Management in Network Slicing
AU - Li, Rongpeng
AU - Zhao, Zhifeng
AU - Sun, Qi
AU - Chi-Lin, I.
AU - Yang, Chenyang
AU - Chen, Xianfu
AU - Zhao, Minjian
AU - Zhang, Honggang
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - 5G mobile communication
KW - Deep Reinforcement Learning
KW - Network slicing
KW - Network Slicing
KW - Neural networks
KW - Neural Networks
KW - Q-Learning
KW - Quality of experience
KW - Resource management
KW - Resource Management
UR - http://www.scopus.com/inward/record.url?scp=85056721900&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2018.2881964
DO - 10.1109/ACCESS.2018.2881964
M3 - Article
VL - 6
SP - 74429
EP - 74441
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 8540003
ER -