Adaptive Scheduling and Trajectory Design for Power-Constrained Wireless UAV Relays

This paper investigates the adaptive trajectory and communication scheduling design for an unmanned aerial vehicle (UAV) relaying random data traffic generated by ground nodes to a base station. The goal is to minimize the expected average communication delay to serve requests, subject to an average UAV mobility power constraint. It is shown that the problem can be cast as a semi-Markov decision process with a two-scale structure, which is optimized efficiently: in the outer decision, the UAV radial velocity for waiting phases and end radius for communication phases optimize the average long-term delay-power trade-off; given outer decisions, inner decisions greedily minimize the instantaneous delay-power cost, yielding the optimal angular velocity in waiting states, and the optimal relay strategy and UAV trajectory in communication states. A constrained particle swarm optimization algorithm is designed to optimize these trajectory problems, demonstrating 100x faster computational speeds than successive convex approximation methods. Simulations demonstrate that an intelligent adaptive design exploiting realistic UAV mobility features, such as helicopter translational lift, reduces the average communication delay and UAV mobility power consumption by 44% and 7%, respectively, with respect to an optimal hovering strategy and by 2% and 13%, respectively, with respect to a greedy delay minimization scheme.Comment: Submitted to IEEE Transactions on Wireless Communication

Drone-assisted emergency communications

Drone-mounted base stations (DBSs) have been proposed to extend coverage and improve communications between mobile users (MUs) and their corresponding macro base stations (MBSs). Different from the base stations on the ground, DBSs can flexibly fly over and close to MUs to establish a better vantage for communications. Thus, the pathloss between a DBS and an MU can be much smaller than that between the MU and MBS. In addition, by hovering in the air, the DBS can likely establish a Line-of-Sight link to the MBS. DBSs can be leveraged to recover communications in a large natural disaster struck area and to fully embody the advantage of drone-assisted communications. In order to retrieve signals from MUs in a large disaster struck area, DBSs need to overcome the large pathloss incurred by the long distance between DBSs and MBSs. This can be addressed by the following two strategies. First, placing multiple drones in a disaster struck area can be used to mitigate the problem of large backhaul pathloss. In this method, data from MUs in the disaster struck area may be forwarded by more than one drone, i.e., DBSs can enable drone-to-drone communications. Thus, the throughput from the disaster struck area can potentially be enhanced by this multi-drone strategy. A cooperative DBS placement and channel allocation algorithm is proposed to maximize the aggregated data rate from MUs in a disaster struck area. It is demonstrated by simulations that the aggregated data rate can be improved by more than 10%, as compared to the scenario without drone-to-drone communications. Second, free space optics (FSO) can be used as backhaul links to reduce the backhaul pathloss. FSO can provision a high-speed point-to-point transmission and is thus suitable for backhaul transmission. A heuristic algorithm is proposed to maximize the number of MUs that can be served by the drones by optimizing user association, DBS placement and spectrum allocation iteratively. It is demonstrated by simulations that the proposed algorithm can cover over 15% more MUs at the expense of less than 5% of the aggregated throughput. Equipping DBSs and MBSs with FSO transceivers incurs extra payload for DBSs, hence shortening the hovering time of DBSs. To prolong the hovering time of a DBS, the FSO beam is deployed to facilitate simultaneous communications and charging. The viability of this concept has been studied by varying the distance between a DBS and an MBS, in which an optimal location of the DBS is found to maximize the data throughput, while the charging power directed to the DBS from the MBS diminishes with the increasing distance between them. Future work is planned to incorporate artificial intelligence to enhance drone-assisted networking for various applications. For example, a drone equipped with a camera can be used to detect victims. By analyzing the captured pictures, the locations of the victims can be estimated by some machine learning based image processing technology