Providing an energy efficient UAV BS positioning mechanism to improve wireless connectivity

As wireless communication continues to advance, the move towards Sixth-Generation (6G) networks has heightened the need for faster data speeds and reliable connections, prompting new approaches to connectivity. In scenarios such as natural disasters, where Ground Base Stations (GBSs) may be compromi...

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Vydáno v:Ad hoc networks Ročník 170; s. 103767
Hlavní autoři: Pasandideh, Faezeh, Najafzadeh, Alireza, Javidi da Costa, João Paulo, Almeida Santos, Giovanni, Valle de Lima, Daniel, Pignaton de Freitas, Edison
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.04.2025
Témata:
Deployment problem
JAYA optimization algorithm
Non-linear optimization
UAV base station
JAYA optimization algorithm
UAV base station
Deployment problem
Non-linear optimization
ISSN:1570-8705, 1570-8713
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Shrnutí:As wireless communication continues to advance, the move towards Sixth-Generation (6G) networks has heightened the need for faster data speeds and reliable connections, prompting new approaches to connectivity. In scenarios such as natural disasters, where Ground Base Stations (GBSs) may be compromised, the use of Unmanned Aerial Vehicles (UAVs) has become increasingly important. A promising approach is to deploy low-altitude UAVs equipped with compact Base Stations (BSs) to reestablish essential communication networks and offer temporary coverage. However, identifying the optimal locations for these UAV-BSs presents a complex challenge. This paper proposes an innovative solution using UAVs as base stations (UAV-BSs) and introduces a Mixed-Integer Non-Linear Programming (MINLP) optimization model to position UAV-BSs based on real-time demand and network conditions. Traditional methods struggle with the complexity of UAV-BS deployment, so a novel algorithm combining the JAYA optimization technique is used. Extensive experiments show this approach maximizes network coverage and connectivity while minimizing UAV-BS power consumption, outperforming other methods in placement accuracy, power usage, packet loss, and latency. The algorithm also adapts to varying network conditions, making it a valuable tool for optimizing UAV-BS locations in dynamic environments.
ISSN:1570-8705
1570-8713
DOI:10.1016/j.adhoc.2025.103767