Autonomous unmanned aerial vehicles (UAVs), spurred by rapid innovation in drone hardware and regulatory frameworks during the last decade, are envisioned for a multitude of applications in service of the society of the future. From the perspective of next-generation wireless networks, UAVs are not only anticipated in the role of passive cellular-connected users, but also as active enablers of connectivity as part of UAV-aided networks. The defining advantage of UAVs in all potential application scenarios is their mobility. To take full advantage of their capabilities, flexible and efficient path planning methods are necessary. This thesis focuses on exploring machine learning (ML), specifically reinforcement learning (RL), as a promising class of solutions to UAV mobility management challenges. Deep RL is one of the few frameworks that allows us to tackle the complex task of UAV control and deployment in communication scenarios directly, given that these are generally NP-hard optimization problems and badly affected by non-convexity. Furthermore, deep RL offers the possibility to balance multiple objectives of UAV-aided networks in a straightforward way, it is very flexible in terms of the availability of prior or model information, while deep RL inference is computationally efficient. This thesis also explores the challenges of severely limited flying time, cooperation between multiple UAVs, and reducing the training data demand of DRL methods. The thesis also explores the connection between drone-assisted networks and robotics, two generally disjoint research communities.
Machine learning methods for UAV-aided wireless networks
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