Optimal Unmanned Aerial Vehicle Control and Designs for Load Balancing in Intelligent Wireless Communication Systems

Maintaining reliable wireless connectivity is essential for the continuing growth of mobile devices and their massive access to the Internet of Things (IoT). However, terrestrial cellular networks often fail to meet their required quality of service (QoS) demand because of the limited spectrum capacity. Although the deployment of more base stations (BSs) in a concerned area is costly and requires regular maintenance. Alternatively, unmanned aerial vehicles (UAVs) could be a potential solution due to their ability of on-demand coverage and the high likelihood of strong line-of-sight (LoS) communication links. Therefore, this chapter focuses on a UAV’s deployment and movement design that supports existing BSs by reducing data traffic load and providing reliable wireless communication. Specifically, we design UAV’s deployment and trajectory under an efficient resource allocation strategy, i.e., assigning devices’ association indicators and transmitting power to maximize overall system’s throughput and minimize the total energy consumption of all devices. For these implementations, we adopt reinforcement learning framework because it does not require all information about the system environment. The proposed methodology finds optimal policy using the Markov decision process, exploiting the previous environment interactions. Our proposed technique significantly improves the system’s performance compared to the other benchmark schemes