Deploying unmanned aerial vehicles (UAVs) as aerial base stations (BSs) in order to assist terrestrial connectivity, has drawn significant attention in recent years. UAV-BSs can take over quickly as service providers during natural disasters and many other emergency situations when ground BSs fail in an unanticipated manner. UAV-BSs can also provide cost-effective Internet connection to users who are out of infrastructure. UAV-BSs benefit from their mobility nature that enables them to change their 3D locations if the demand of ground users changes. In order to effectively make use of the mobility of UAV-BSs in a dynamic network and maximize the performance, the 3D location of UAV-BSs should be continuously optimized. However, solving the location optimization problem of UAV-BSs is NP-hard with no optimal solution in polynomial time for which near optimal solutions have to be exploited. Besides the conventional solutions, i.e. heuristic solutions, machine learning (ML), specifically reinforcement learning (RL), has emerged as a promising solution for tackling the positioning problem of UAV-BSs. The common practice for optimizing the 3D location of UAV-BSs using RL algorithms is assuming fixed location of ground users (i.e., UEs) in addition to defining discrete and limited action space for the agent of RL.
In this thesis, we focus on improving the location optimization of UAV-BSs in two ways: 1-Taking into account the mobility of users in the design of RL algorithm, 2-Extending the action space of RL to a continuous action space so that the UAV-BS agent can flexibly change its location by any distance (limited by the maximum speed of UAV-BS).
Three types of RL algorithms, i.e. Q-learning (QL), deep Q-learning (DQL) and actor-critic deep Q-learning (ACDQL) have been employed in this thesis to step-by-step improve the performance results of a UAV-assisted cellular network. QL is the first type of RL algorithm we use for the autonomous movement of the UAV-BS in the presence of mobile users. As a solution to the limitations of QL, we next propose a DQL-based strategy for the location optimization of the UAV-BS which largely improves the performance results of the network compared to the QL-based model. Third, we propose an ACDQL-based solution for autonomously moving the UAV-BS in a continuous action space wherein the performance results significantly outperforms both QL and DQL strategies.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/44418 |
| Date | 21 December 2022 |
| Creators | Parvaresh, Nahid |
| Contributors | Kantarci, Burak |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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