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Learning to Charge RF-Energy Harvesting Devices in WiFi Networks
arXiv - CS - Systems and Control Pub Date : 2020-05-25 , DOI: arxiv-2005.12022
Yizhou Luo and Kwan-Wu Chin

In this paper, we consider a solar-powered Access Point (AP) that is tasked with supporting both non-energy harvesting or legacy data users such as laptops, and devices with Radio Frequency (RF)-energy harvesting and sensing capabilities. We propose two solutions that enable the AP to manage its harvested energy via transmit power control and also ensure devices perform sensing tasks frequently. Advantageously, our solutions are suitable for current wireless networks and do not require perfect channel gain information or non-causal energy arrival at devices. The first solution uses a deep Q-network (DQN) whilst the second solution uses Model Predictive Control (MPC) to control the AP's transmit power. Our results show that our DQN and MPC solutions improve energy efficiency and user satisfaction by respectively 16% to 35%, and 10% to 42% as compared to competing algorithms.

中文翻译:

学习为 WiFi 网络中的射频能量收集设备充电

在本文中,我们考虑了一种太阳能接入点 (AP),它的任务是支持非能量收集或传统数据用户(如笔记本电脑)以及具有射频 (RF) 能量收集和传感功能的设备。我们提出了两种解决方案,使 AP 能够通过传输功率控制管理其收集的能量,并确保设备频繁执行传感任务。有利地,我们的解决方案适用于当前的无线网络并且不需要完美的信道增益信息或非因果能量到达设备。第一个解决方案使用深度 Q 网络 (DQN),而第二个解决方案使用模型预测控制 (MPC) 来控制 AP 的发射功率。我们的结果表明,我们的 DQN 和 MPC 解决方案将能源效率和用户满意度分别提高了 16% 到 35%,
更新日期:2020-05-26
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