Smart Power Control for Quality-Driven Multi-User Video Transmissions: A Deep Reinforcement Learning Approach
Smart Power Control for Quality-Driven Multi-User Video Transmissions: A Deep Reinforcement Learning Approach
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DOI：
10.1109/access.2019.2961914
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Ticao Zhang;S. Mao
中科院分区：
文献类型：
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作者：
Ticao Zhang;S. Mao
设备到设备（D2D）通信已被视为满足5G网络中急剧增加的视频数据需求的有前景的技术。本文研究了多用户视频传输系统中的功率控制问题。由于优化问题的非凸性质，获得最优策略具有挑战性。此外，许多现有的解决方案需要每个链路的瞬时信道状态信息（CSI），这在资源有限的无线网络中是难以获得的。我们开发了一种基于多智能体深度强化学习的功率控制方法，其中每个智能体根据观察到的局部状态自适应地控制其发射功率。该方法的目标是最大限度地提高接收到的视频的所有用户的平均质量，同时满足每个用户的质量要求。在离线训练之后，该方法可以分布式地实现，使得所有用户可以从任何初始状态达到其目标状态。与传统的基于优化的方法相比，所提出的方法是无模型的，不需要CSI，并可扩展到大型网络。
Device-to-device (D2D) communications have been regarded as a promising technology to meet the dramatically increasing video data demand in the 5G network. In this paper, we consider the power control problem in a multi-user video transmission system. Due to the non-convex nature of the optimization problem, it is challenging to obtain an optimal strategy. In addition, many existing solutions require instantaneous channel state information (CSI) for each link, which is hard to obtain in resource-limited wireless networks. We developed a multi-agent deep reinforcement learning-based power control method, where each agent adaptively controls its transmit power based on the observed local states. The proposed method aims to maximize the average quality of received videos of all users while satisfying the quality requirement of each user. After off-line training, the method can be distributedly implemented such that all the users can achieve their target state from any initial state. Compared with conventional optimization based approach, the proposed method is model-free, does not require CSI, and is scalable to large networks.