Abstract
This paper considers data upload from multiple devices to a rotary-wing Unmanned Aerial Vehicle (UAV) equipped with a Successive Interference Cancellation (SIC) radio. The problem at hand is to optimize the UAV's data collection points, and the set of transmitting devices at each point. We outline three contributions. The first is an Integer Linear Program (ILP), which can be used to compute the optimal trajectory and data transmission schedule. Second, we propose a novel heuristic called Iteratively Construct Link Schedule and Trajectory (ICLST) that includes a link set/schedule selection policy called Highest Sum-Rate Selection (HSRS). Third, we propose a novel learning-based protocol that enables a UAV to independently learn the optimal trajectory with the highest energy efficiency. Our results show that a SIC radio helps double the amount of data collected by the UAV. Placing devices at different heights helps the UAV collect more data. Moreover, ICLST with HSRS is capable of producing a schedule that is near optimal. Additionally, our learning protocol yields a schedule with the highest energy-efficiency.
Highlights
Mobile vehicles are envisaged to augment existing communication infrastructures in order to improve the services provided to users
This paper considers two approaches to maximize the number of devices transmitting to the Unmanned Aerial Vehicle (UAV) or uplinks at each data collection point
There are no prior works have considered deriving the optimal trajectory for a mobile UAV and uplink transmission set at each data collection point, where the links in each transmission must satisfy Successive Interference Cancellation (SIC) constraints
Summary
Mobile vehicles are envisaged to augment existing communication infrastructures in order to improve the services provided to users. We seek data collection points that allow a high number of SIC decoding successes or simultaneous uplink transmissions [7] This means the trajectory selected by the UAV will affect the resulting uplinks transmission schedule. We propose a novel learning based protocol that is based on State-Action-RewardState-Action (SARSA) [8] This protocol allows the UAV to independently learn a trajectory and the corresponding link schedule that maximize the amount of collected data and minimize its energy usage. To the best of our knowledge, this is the first work that considers the combinatoric problem of selecting data collection points and transmission sets in order to maximize the total data collected by a SIC-capable UAV; see Section II for details.
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