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AI Assisted Mobility Management for Cellular Connected UAVs

Time: Fri 2025-01-31 10.00

Location: Ka-Sal C, Kistangangen 16, Kista

Video link: https://kth-se.zoom.us/j/61632995144

Language: English

Subject area: Telecommunication

Doctoral student: Irshad Ahmad Meer , Kommunikationssystem, CoS

Opponent: Professor Sinem Sinem Coleri, Koc University

Supervisor: Cicek Cavdar, Radio Systems Laboratory (RS Lab); Mustafa Özger, Radio Systems Laboratory (RS Lab); Ki Won Sung, Radio Systems Laboratory (RS Lab)

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QC 20250102

Abstract

Unmanned Aerial Vehicles (UAVs) connected to cellular networks, i.e., cellular-connected UAVs, introduce unique challenges and opportunities in mobility management that distinguish them from terrestrial users. This thesis presents a comprehensive approach for optimizing UAV integration into cellular networks.

We first investigate the distinct mobility management needs for cellular-connected UAVs. Unlike terrestrial mobility management, which primarily focuses on preventing radio link failures at cell edges, UAVs experience fragmented and overlapping coverage, often with line-of-sight visibility to multiple ground base stations (BSs). Consequently, UAV mobility management must address not only link stability but also the minimization of unnecessary handovers with sustained service availability, particularly in uplink scenarios.To tackle these challenges, we propose two solutions, a model-based handover parameter optimization algorithm and a model-free deep reinforcement learning (DRL) based handover algorithm, both designed specifically for UAV mobility management.We extend the problem by integrating UAV path planning with wireless objectives, including interference management, delay reduction, and minimized handovers. This results in a joint optimization framework for UAV trajectory planning, handover management, and radio resource allocation. To solve this multi-objective problem, we develop a multi-agent DRL algorithm that combines mission-specific trajectory planning with network-driven adjustments, optimizing resource allocation and handover transitions.

Furthermore, we address mobility management in multi-connectivity scenarios where UAVs are served by clusters of distributed BSs. As UAVs move, the serving BS clusters must be dynamically reconfigured, necessitating coordinated resource allocation under stringent and time-sensitive reliability constraints. We propose a centralized, fully distributed, and hierarchical DRL-based approaches to achieve reliable connectivity, reduce power consumption, and minimize cluster reconfiguration frequency.

Lastly, to evaluate a network’s capability to support range-based localization for cellular-connected UAVs, we introduce an analytical framework. This framework defines B-localizability as the probability of a UAV receiving sufficient localization signals from at least B ground BSs, meeting a specific Signal-to-Interference-plus-Noise Ratio (SINR) threshold. By incorporating UAV parameters within a three-dimensional environment, we provide insights into localizability factors such as distance distributions, path loss, interference, and SINR. 

urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-358036