The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs
Abstract
Cloud Radio Access Networks (RANs) have become a key enabling technique for the next generation (5G) wireless communications, which can meet requirements of massively growing wireless data traffic. However, resource allocation in cloud RANs still needs to be further improved in order to reach the objective of minimizing power consumption and meeting demands of wireless users over a long operational period. Inspired by the success of Deep Reinforcement Learning (DRL) on solving complicated control problems, we present a novel DRL-based framework for power-efficient resource allocation in cloud RANs. Specifically, we define the state space, action space and reward function for the DRL agent, apply a Deep Neural Network (DNN) to approximate the action-value function, and formally formulate the resource allocation problem (in each decision epoch) as a convex optimization problem. We evaluate the performance of the proposed framework by comparing it with two widely-used baselines via simulation. The simulation results show it can achieve significant power savings while meeting user demands, and it can well handle highly dynamic cases.
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Cites
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Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Deep Reinforcement Learning Based Resource Allocation for V2V Communications
Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2897134 · Cited by 870
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
Mao, Q., Hu, F., & Hao, Q. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2846401 · Cited by 789
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2924243 · Cited by 678
Deep reinforcement learning based computation offloading and resource allocation for MEC
Li, J., Gao, H., Lv, T., & Lu, Y. (2018) · 2018 IEEE Wireless Communications and Networking Conference (WCNC) · doi:10.1109/wcnc.2018.8377343 · Cited by 548
Metadata
- Authors
- Xu, Z., Wang, Y., Tang, J., Wang, J., & Gursoy, M. C.
- Venue
- 2017 IEEE International Conference on Communications (ICC)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 1-6
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W2741401130
- Cited by
- 262
- References
- 7
- FWCI
- 52.54
- Type
- conference paper
- Language
- en
- Open access
- closed
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