UAV Trajectory Planning Optimization in Beyond 5G Networks

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

Integrating unmanned aerial vehicles (UAVs) as flying base stations (FBSs) is expected to be an important architectural element in beyond 5G (B5G) mobile wireless networks. A key operational aspect in UAV-aided B5G networks is the UAV trajectory optimization as it is also commonly referred to path planning.

This thesis proposes several UAV trajectory planning optimization strategies in various networks for different use cases: 1) a trajectory planning optimization in a multi-cell setting with terrestrial base stations (BSs) that hosts the UAV, 2) an interference aware trajectory planning optimization for multiple UAVs in both a single cell and a multi-cell network, 3) an energy efficient trajectory planning in a relay network for backhauling to bridge end-to-end connection, 4) an energy fairness trajectory planning for multiple UAVs and a geographical division (GD) clustering algorithm, 5) a placement optimization of robotic aerial small cells (RASCs) with grasping end effectors for network flow in a millimeter wave (mmWave) backhaul network.

The proposed optimal schemes mentioned above are formulated as various Mixed Integer Linear Programming (MILP) models. Since finding such the optimal solutions is computationally expensive, this thesis, therefore, also proposes a variety of heuristic algorithms to ease the computational complexity.

A wide set of simulation results confirms that the proposed optimal schemes outperform previously proposed nominal strategies, and the proposed heuristic approaches provide highly competitive decision making for real time implementation.


Date of Award1 Dec 2022
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorMohammad Nakhai (Supervisor) & Vasilis Friderikos (Supervisor)

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