This paper presents a deep reinforcement learning (DRL) solution for power control in wireless communications, describes its embedded implementation with WiFi transceivers for a WiFi network system, and evaluates the performance with high-fidelity emulation tests. In a multi-hop wireless network, each mobile node measures its link quality and signal strength, and controls its transmit power. As a model-free solution, reinforcement learning allows nodes to adapt their actions by observing the states and maximize their cumulative rewards over time. For each node, the state consists of transmit power, link quality and signal strength; the action adjusts the transmit power; and the reward combines energy efficiency (throughput normalized by energy consumption) and penalty of changing the transmit power. As the state space is large, Q-learning is hard to implement on embedded platforms with limited memory and processing power. By approximating the Q-values with a DQN, DRL is implemented for the embedded platform of each node combining an ARM processor and a WiFi transceiver for 802.11n. Controllable and repeatable emulation tests are performed by inducing realistic channel effects on RF signals. Performance comparison with benchmark schemes of fixed and myopic power allocations shows that power control with DRL provides major improvements to energy efficiency and throughput in WiFi network systems.

翻译：本文展示了无线通信电源控制的深度强化学习(DRL)解决方案,描述了它与无线网络系统WiFi收发器的嵌入式实施,并评估了高纤维模拟测试的性能。在多点无线网络中,每个移动节点测量其链接质量和信号强度,并控制其传输电力。作为无模式解决方案,强化学习允许节点通过观察各州来调整其行动,并随着时间的推移最大限度地获得累积的回报。对于每个节点,国家包括传输电力、连接质量和信号强度;行动调整传输电力;以及奖励将能效(通过能源消耗实现通量正常化)和改变传输电源的处罚结合起来。由于州空间很大,Q学习很难在内存和处理能力有限的嵌入平台上实施。作为一个无模式解决方案,强化学习允许节点通过观察DQN调整其Q值来调整其行动。对于每个节点的嵌入平台实施DRL,DR为802.11n组合一个亚美尼亚进程和WiFi转发器转发器;控制和可重复的电源传输能力,通过现实化系统测试我的节能效率测试,使我的网络能定位定位系统通过定位定位定位定位定位定位定位定位定位定位定位定位定位定位定位系统进行定位控制。