The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches
Abstract
The model-based power allocation has been investigated for decades, but this approach requires mathematical models to be analytically tractable and it has high computational complexity. Recently, the data-driven model-free approaches have been rapidly developed to achieve near-optimal performance with affordable computational complexity, and deep reinforcement learning (DRL) is regarded as one such approach having great potential for future intelligent networks. In this paper, a dynamic downlink power control problem is considered for maximizing the sum-rate in a multi-user wireless cellular network. Using cross-cell coordinations, the proposed multi-agent DRL framework includes off-line and on-line centralized training and distributed execution, and a mathematical analysis is presented for the top-level design of the near-static problem. Policy-based REINFORCE, value-based deep Q-learning (DQL), actor-critic deep deterministic policy gradient (DDPG) algorithms are proposed for this sum-rate problem. Simulation results show that the data-driven approaches outperform the state-of-art model-based methods on sum-rate performance. Furthermore, the DDPG outperforms the REINFORCE and DQL in terms of both sum-rate performance and robustness.
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Metadata
- Authors
- Meng, F., Chen, P., Wu, L., & Cheng, J.
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2020-10
- Volume
- 19
- Issue
- 10
- Pages
- 6255-6267
- ISSN
- 1536-1276 (ISSN-L)
- 1558-2248
- OpenAlex
- W3036160090
- Cited by
- 288
- References
- 9
- FWCI
- 13.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Funders
- National Natural Science Foundation of China (61801112, 61601281)
- Natural Science Foundation of Jiangsu Province (BK20180357)
- foundation of Shannxi Key Laboratory of Integrated and Intelligent Navigation (SKLIIN-20190204)
- Open Program of State Key Laboratory of Millimeter Waves, Southeast University (K202029)
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- Meng, F., Chen, P., Wu, L., & Cheng, J. (2020). Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches. IEEE Transactions on Wireless Communications. https://doi.org/10.1109/twc.2020.3001736
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