Title

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

Publication Name

IEEE Communications Letters

Abstract

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.

Open Access Status

This publication is not available as open access

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Link to publisher version (DOI)

http://dx.doi.org/10.1109/LCOMM.2021.3108227