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Publication
Reinforcement Learning for Scheduling Wireless Powered Sensor Communications
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| 1.33 MB | Adobe PDF |
Advisor(s)
Abstract(s)
In a wireless powered sensor network, a base station
transfers power to sensors by using wireless power transfer
(WPT). Inadequately scheduling WPT and data transmission
causes fast battery drainage and data queue overflow of some
sensors who could have potentially gained high data reception. In
this paper, scheduling WPT and data transmission is formulated
as a Markov decision process (MDP) by jointly considering sensors’
energy consumption and data queue. In practical scenarios,
the prior knowledge about battery level and data queue length in
MDP is not available at the base station. We study reinforcement
learning at the sensors to find a transmission scheduling strategy,
minimizing data packet loss. An optimal scheduling strategy
with full-state information is also investigated, assuming that
the complete battery level and data queue information are well
known by the base station. This presents the lower bound of the
data packet loss in wireless powered sensor networks. Numerical
results demonstrate that the proposed reinforcement learning
scheduling algorithm significantly reduces network packet loss
rate by 60%, and increases network goodput by 67%, compared
to existing non-MDP greedy approaches. Moreover, comparing
the optimal solutions, the performance loss due to the lack of
sensors’ full-state information is less than 4.6%.
Description
Keywords
Wireless sensor network Wireless power transfer Markov decision process Reinforcement learning Optimization