We consider a mobile network in which traditional static terrestrial base stations are not capable of completely serving the existing user demand, due to the huge number of connected devices. In this setting, an equipped Unmanned Aerial Vehicle (UAV) can be employed to provide network connection where needed in a flexible way, thereby acting as an unmanned aerial base station. The goal is to determine the best UAV trajectory in order to serve as many users as possible. The UAV can move at different speeds and can serve users within its communication range, although the data rate depends on the positions of UAV and users. In addition, each user has a demand (e.g., the number of bits the user wants to download/upload from/to the network) and a time window during which requires the service. We propose a Biased Random-Key Genetic Algorithm (BRKGA) and a Simulated Annealing Algorithm (SAA), and compare them on realistic instances with more than 500 users in different settings.

Cacchiani V., Ceschia S., Mignardi S., Buratti C. (2023). Metaheuristic Algorithms for UAV Trajectory Optimization in Mobile Networks. Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-26504-4_3].

Metaheuristic Algorithms for UAV Trajectory Optimization in Mobile Networks

Cacchiani V.;Mignardi S.;Buratti C.
2023

Abstract

We consider a mobile network in which traditional static terrestrial base stations are not capable of completely serving the existing user demand, due to the huge number of connected devices. In this setting, an equipped Unmanned Aerial Vehicle (UAV) can be employed to provide network connection where needed in a flexible way, thereby acting as an unmanned aerial base station. The goal is to determine the best UAV trajectory in order to serve as many users as possible. The UAV can move at different speeds and can serve users within its communication range, although the data rate depends on the positions of UAV and users. In addition, each user has a demand (e.g., the number of bits the user wants to download/upload from/to the network) and a time window during which requires the service. We propose a Biased Random-Key Genetic Algorithm (BRKGA) and a Simulated Annealing Algorithm (SAA), and compare them on realistic instances with more than 500 users in different settings.
2023
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
30
44
Cacchiani V., Ceschia S., Mignardi S., Buratti C. (2023). Metaheuristic Algorithms for UAV Trajectory Optimization in Mobile Networks. Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-26504-4_3].
Cacchiani V.; Ceschia S.; Mignardi S.; Buratti C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/956064
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