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Machine Learning-Based 5G RAN Slicing for Broadcasting Services
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AI
DOI：10.1109/TBC.2021.3122353.png)
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摘要
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Along with the commercialization of evolved multimedia broadcast multicast services (eMBMS), the number of mobile broadcasting users is growing notably. Previous works reveal that the accuracy of mobile channel estimation will significantly impact the quality of broadcasting services. Motivated by this fact, we apply machine learning (ML) to the fifth-generation Radio Access Network (5G RAN) slicing in this paper for the estimation and the prediction of the channel status in mobile scenarios. More specifically, a cascaded convolutional neural network (CNN)-long short term memory network (LSTM) architecture is developed to achieve channel estimation for mobile broadcasting users. The energy efficiency of the base station (BS) is modeled mathematically, and the sub-optimal solution is achieved by deep Q-Network (DQN) based on the available channel status. Finally, we present the simulation results to justify the performance of our proposed schemes.
Keyword:
5G mobile communication
Network slicing
Broadcasting
Vehicle dynamics
Resource management
Channel estimation
Predictive models
Machine learning
5G RAN slicing
deep Q-Network
期刊
IF:
4.8
论文数:
2.1K
被引数:
3.0K
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引用论文
分享收藏A Service-Oriented Deployment Policy of End-to End Network Slicing Based on Complex Network Theory.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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