Learning to Charge RF-Energy Harvesting Devices in WiFi Networks

• URL: http://arxiv.org/abs/2005.12022v1
• Date: Mon, 25 May 2020 10:55:24 GMT
• Title: Learning to Charge RF-Energy Harvesting Devices in WiFi Networks
• Authors: Yizhou Luo and Kwan-Wu Chin
• Abstract summary: We propose two solutions that enable the AP to manage its harvested energy via transmit power control. 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 16% to 35%, and 10% to 42% as compared to competing algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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