QoE-driven multi-UAV deployment scheme for emergency communication networks

When ground communication infrastructure within a region cannot be used for some reason, deploying unmanned aerial vehicle (UAV)-mounted base stations is undoubtedly the most effective way to provide communication services. This paper investigates the problem of joint deployment and power allocation of multiple UAVs, where ground terminals (GTs) seek to maximize quality of experience (QoE). In the mixed line-of-sight and non-line-of-sight environment, UAVs need to change position in order to collect channel information until deployment problem is solved. Moreover, only neighboring UAVs communicate with each other, which makes the problem more difficult to solve. In order to solve this problem, game theory is used to model this problem and design a distributed learning algorithm to maximize the QoE of all GTs in the entire system. Simulation results validate the effectiveness of the proposed learning algorithm in improving the QoE fairness and achieving the rapid deployment of UAVs. This paper investigates the problem of joint deployment and power allocation of multiple UAVs with the purpose of improving the quality of experience (QoE) of all ground terminals (GTs). In order to solve this problem, we use game theory to model this problem and design a distributed learning algorithm to maximize the QoE of all GTs in UAV-assisted emergency communication networks. image