Deep Q-Learning based Resource Management in UAV-assisted Wireless Powered IoT Networks
Ref: CISTER-TR-200201 Publication Date: 7 to 11, Jun, 2020
Deep Q-Learning based Resource Management in UAV-assisted Wireless Powered IoT Networks
Ref: CISTER-TR-200201 Publication Date: 7 to 11, Jun, 2020Abstract:
In Unmanned Aerial Vehicle (UAV)-assisted
Wireless Powered Internet of Things (IoT), the UAV is
employed to charge the IoT nodes remotely viaWireless Power
Transfer (WPT) and collect their data. A key challenge of
resource management for WPT and data collection is preventing
battery drainage and buffer overflow of the ground IoT
nodes in the presence of highly dynamic airborne channels. In
this paper, we consider the resource management problem in
practical scenarios, where the UAV has no a-prior information
on battery levels and data queue lengths of the nodes. We
formulate the resource management of UAV-assisted WPT
and data collection as Markov Decision Process (MDP), where
the states consist of battery levels and data queue lengths of
the IoT nodes, channel qualities, and positions of the UAV.
A deep Q-learning based resource management is proposed
to minimize the overall data packet loss of the IoT nodes,
by optimally deciding the IoT node for data collection and
power transfer, and the associated modulation scheme of the
IoT node.
Kai Li
Wei Ni
Eduardo TovarEvents:
IEEE International Conference on Communications (ICC 2020), pp 1-6.
Online.
DOI:10.1109/ICC40277.2020.9149282.
ISBN: 978-1-7281-5089-5.
ISSN: 1938-1883.
Record Date: 4, Feb, 2020