Common Throughput Maximization for UAV-Enabled Interference Channel with Wireless Powered Communications
This paper studies an unmanned aerial vehicle (UAV)-enabled two-user interference channel for wireless powered communication networks (WPCNs), in which two UAVs wirelessly charge two low-power Internet-of-things (IoT)-devices on the ground and collect information from them. We consider two scenarios...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
10.10.2019
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Subjects | |
Online Access | Get full text |
DOI | 10.48550/arxiv.1910.04403 |
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Summary: | This paper studies an unmanned aerial vehicle (UAV)-enabled two-user
interference channel for wireless powered communication networks (WPCNs), in
which two UAVs wirelessly charge two low-power Internet-of-things (IoT)-devices
on the ground and collect information from them. We consider two scenarios when
both UAVs cooperate in energy transmission and/or information reception via
interference coordination and coordinated multi-point (CoMP), respectively. For
both scenarios, the UAVs' controllable mobility is exploited via trajectory
design to not only enhance the wireless power transfer (WPT) efficiency in the
downlink, but also mitigate the co-channel interference for wireless
information transfer (WIT) in the uplink. In particular, the objective is to
maximize the uplink common (minimum) throughput of the two IoT-devices over a
finite UAV mission period, by jointly optimizing the trajectories of both UAVs
and the downlink/uplink wireless resource allocation, subject to the maximum
flying speed and collision avoidance constraints at UAVs, as well as the
individual energy neutrality constraints at IoT-devices. Under both scenarios
of interference coordination and CoMP, we first obtain the optimal solutions to
the two common-rate maximization problems in well structures for the special
case with sufficiently long UAV mission duration. Next, we obtain high-quality
solutions for the practical case with finite UAV mission duration by using the
alternating optimization and successive convex approximation (SCA). |
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DOI: | 10.48550/arxiv.1910.04403 |