A Distributed Device Selection Method to Minimize AoI in RF-Charging Networks

• Published in: IEEE communications letters Vol. 25; no. 11; pp. 3733 - 3737
• Main Authors: Zhang, Lei, Chin, Kwan-Wu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Batteries; device selection; Energy harvesting; Information freshness; learning; Logic gates; Machine learning; Markov processes; Optimization; Radio frequency; Schedules; Signal to noise ratio; Uplink; wireless power
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2021.3108227

This letter considers optimizing information freshness in a network with Radio Frequency (RF)-energy harvesting wireless devices. A Hybrid Access Point (HAP) charges these devices and instructs a subset of devices to carry out sampling and transmit their sample. We outline a Distributed Q-Learning (DQL) algorithm that allows the HAP to select devices without knowing their uplink channel state and battery state. Our results show that DQL achieves at most 48%, 57%, and 61% lower average AoI than Round Robin (RR), Random Pick (RP), and AoI-Greedy (AG), respectively. The average AoI of DQL is only around 7% higher than the optimal selection strategy.