期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 19, 期 10, 页码 6255-6267出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.3001736
关键词
Optimization; Heuristic algorithms; Resource management; Mathematical model; Wireless communication; Computational modeling; Interference; Deep reinforcement learning; deep deterministic policy gradient; policy-based; interfering broadcast channel; power control; resource allocation
资金
- 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 [SKLIIN20190204]
- Open Program of State Key Laboratory of Millimeter Waves, Southeast University [K202029]
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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