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Due to the high maneuverability of unmanned aerial vehicle (UAV), a cluster of UAVs is considered used to collect sensing data from the sensors that distributed randomly in an area without the terrestrial infrastructure. The cluster members work as relays to forward the sensing data from sensors to the cluster head. For the reason that the relay deployment impacts the transmission rate and coverage area directly, we are going to optimize the deployment of the UAV relays, aiming to maximize the total capacity of the network. The problem of multi-relay deployment is intractable for two reasons. On one hand, because of the interactional and coupled relationship among the UAV relays, when the deployment of any given relay changes, the deployment optimization of other relays will be affected. On the other hand, on account of that the exact positions of the sensors are unknown, the deployment optimization of the UAV relays cannot be completed directly because of lacking parameters. In order to tackle the coupled relationship among the UAV relays, the problem of multi-relay deployment is modeled as a local interaction game. We prove that the multi-relay deployment game is an exact potential game that has at least one Nash equilibrium (NE) point. Then, the better reply-based relay deployment approach, which is an online learning approach that does not demand the information of the exact positions of sensors, is proposed to search the NE point. The simulation results show that the network capacity is significantly enhanced with the proposed relays deployment approach. INDEX TERMS UAV relay deployment, potential game, UAV cluster, online learning.

Recently, unmanned aerial vehicles (UAVs) have been widely studied in the communication area to work as aerial base stations, due to the high probability of line of sight (LoS) and high flexibility. However, few works consider fairness for the users, which is one of the most important metrics for a network. In this paper, in order to maximize network capacity with the consideration of fairness, trajectory and scheduling of the mobile UAV aerial base station are jointly optimized. Firstly, the problem of maximizing network capacity with the consideration of fairness is formulated. On account of the coupling relationship of trajectory and scheduling, an alternate iteration approach that contains ant colony algorithm and genetic algorithm are then proposed to solve this intractable problem. Finally, the simulation results demonstrate the fairness enhance of the network and the validity and effectiveness of the proposed optimization approach.

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