Addressing UAV Relay Challenges Using Deep Reinforcement Learning
Ref: CISTER-TR-191205 Publication Date: 17, Dec, 2019
Addressing UAV Relay Challenges Using Deep Reinforcement LearningRef: CISTER-TR-191205 Publication Date: 17, Dec, 2019
The use of unmanned aerial vehicles (UAVs) as ying wireless communication platforms has attracted the attention of researchers over the past few years due to their autonomy,
exibility, and a broad range of application domains. Indeed, UAVs enjoy having a broad range of applications including military, surveillance and monitoring, telecommunications, delivery of medical supplies, and rescue operations. UAVs can be utilized as wireless relays for improving connectivity of ground wireless devices and extending network coverage. Also, UAVs can act as mobile aerial base stations to provide reliable communications for ground users and extend the capacity of wireless networks. The high altitude of UAVs can enable line-of-sight (LoS) communication links to the ground users. Due to the adjustable altitude and mobility, UAVs can move toward potential ground users and establish reliable connections with a low transmit power. Hence, they can provide a cost eective solution to collect data from ground mobile users that are spread around a geographical area with limited terrestrial infrastructure.In this dissertation, we focus on UAV relaying as one use case of UAV-assisted wireless communication, and propose to address three of its main challenges. Scheduling algorithms are important components in the provision of guaranteed quality of service parameters such as delay,jitter,packet loss rate, or throughput. Scheduling has been studied in wireless networks,however the unique characteristics of UAV assisted wireless communications such as, mobility of UAV,timing-varying channel conditions and multi-user diversity,motivate us for the development of new scheduling solution that are specically tailored for this environment. Trajectory optimization is an important design aspect of UAV. For UAV relay systems,trajectory optimization can increase network throughput and substantially shorten the communication distance and hence is critical for high-capacity performance. Optimizing the ight path of UAVs is challenging as it requires considering many physical constraints and parameters.Furthermore, solving a continuous UAV trajectory optimization problem is challenging from analytical point of view since it involves nding an innite number of optimization variables such as UAV's locations. In the realm of security for UAV, GPS spoong is one of the most important cyber attacks. In this attack, the GPS receiver on the UAV is feed with fake signals generated by a malicious user so that UAV is unable to distinguish between the fake signals and the ones receiving from GPS. The fake signals can mislead not only the aircraft but also air trac controllers, leading to serious problems. These problems range from aircraft hijacking to collisions and human casualties. On the other hand, deep reinforcement learning(DRL) has capability to address these emerging issues in UAV-assisted wireless communication through interaction with environment. Indeed, DRL provides an autonomous decision-making mechanism for the network entities to solve non-convex, complex model-free problems. Hence, in this dissertation, we utilize DRL to address the mentioned challenges.
Notes: Comissão de acompanhamento: Comissão Científica PDEEC: Luis Almeida Orientador: Kai Li Coorientador: Eduardo Tovar Elemento da FEUP: Luis Almeida Elemento externo: Xiaoming Fu (University of Göttingen, Germany)