On energy and data delivery in wireless local area networks with RF charging nodes
Wireless charging is now a reality. Low-power devices with sensing capabilities deployed within a building for example can now be powered wirelessly via Radio Frequency (RF) transmissions from existing Access Points (APs) that form a Wireless Local Area Network (WLAN). However, an AP cannot transmit frequently to charge devices as it may starve other nearby APs operating on the same channel. Consequently, there is a need to schedule the transmissions of APs to ensure their data queues remain short whilst charging energy-harvesting devices. We present a finite-horizon Markov Decision Process (MDP) to capture the queue states at APs and also channel conditions to nodes. The reward to be optimized is the amount of delivered energy and data. We investigate the following queue selection rules: max weight, max queue, best channel state and random. Our results show that APs that select the best queue in each time slot according to the max weight rule yields a transmission schedule that has the best reward; i.e., highest delivered packets and energy. Moreover, the obtained reward has the smallest gap to the optimal/exact reward.