Trajectory Optimization for Cooperative Dual-band UAV Swarms
This work addresses efficient communication support for IoT applications using UAV swarms, but it appears incremental as it builds on existing trajectory optimization methods without introducing a fundamentally new paradigm.
The paper tackles the problem of minimizing total service time for bandwidth-hungry, delay-tolerant applications by optimizing UAV trajectories and assignments in dual-band swarms, using an iterative approach that combines mixed linear programming and hierarchical search to enhance performance in selected scenarios.
Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse wireless communication fields. They can act as high-altitude flying relays to support communications between ground nodes due to their ability to provide line-of-sight links. With the flourishing Internet of Things, several types of new applications are emerging. In this paper, we focus on bandwidth hungry and delay-tolerant applications where multiple pairs of transceivers require the support of UAVs to complete their transmissions. To do so, the UAVs have the possibility to employ two different bands namely the typical microwave and the high-rate millimeter wave bands. In this paper, we develop a generic framework to assign UAVs to supported transceivers and optimize their trajectories such that a weighted function of the total service time is minimized. Taking into account both the communication time needed to relay the message and the flying time of the UAVs, a mixed non-linear programming problem aiming at finding the stops at which the UAVs hover to forward the data to the receivers is formulated. An iterative approach is then developed to solve the problem. First, a mixed linear programming problem is optimally solved to determine the path of each available UAV. Then, a hierarchical iterative search is executed to enhance the UAV stops' locations and reduce the service time. The behavior of the UAVs and the benefits of the proposed framework are showcased for selected scenarios.