论文标题
学会在WiFi网络中充电RF - 能量收集设备
Learning to Charge RF-Energy Harvesting Devices in WiFi Networks
论文作者
论文摘要
在本文中，我们考虑了一个由太阳能访问点（AP），其任务是支持非能量收获或旧数据用户，例如笔记本电脑以及带射频（RF）的设备（RF） - 能源收获和感知功能。我们提出了两种解决方案，使AP能够通过发射功率控制来管理其收获的能量，并确保设备经常执行传感任务。有利地，我们的解决方案适用于当前的无线网络，不需要完美的通道增益信息或设备的非毒素能量到达。第一个解决方案使用深Q-NETWORK（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，与竞争算法相比，我们的DQN和MPC解决方案分别提高了16％至35％的能源效率和用户满意度，而10％至42％。
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.
