Version 1
: Received: 5 October 2024 / Approved: 5 October 2024 / Online: 7 October 2024 (11:25:53 CEST)
How to cite:
Basu, A.; Oroojeni, H.; Samakovitis, G.; Al-Rifaie, M. M. 3 Dimensional DC Placement: Drone Placement for Optimal Coverage. Preprints2024, 2024100406. https://doi.org/10.20944/preprints202410.0406.v1
Basu, A.; Oroojeni, H.; Samakovitis, G.; Al-Rifaie, M. M. 3 Dimensional DC Placement: Drone Placement for Optimal Coverage. Preprints 2024, 2024100406. https://doi.org/10.20944/preprints202410.0406.v1
Basu, A.; Oroojeni, H.; Samakovitis, G.; Al-Rifaie, M. M. 3 Dimensional DC Placement: Drone Placement for Optimal Coverage. Preprints2024, 2024100406. https://doi.org/10.20944/preprints202410.0406.v1
APA Style
Basu, A., Oroojeni, H., Samakovitis, G., & Al-Rifaie, M. M. (2024). 3 Dimensional DC Placement: Drone Placement for Optimal Coverage. Preprints. https://doi.org/10.20944/preprints202410.0406.v1
Chicago/Turabian Style
Basu, A., Georgios Samakovitis and Mohammad Majid Al-Rifaie. 2024 "3 Dimensional DC Placement: Drone Placement for Optimal Coverage" Preprints. https://doi.org/10.20944/preprints202410.0406.v1
Abstract
Using Drone Cells to optimize Radio Access Networks is an exemplary way to enhance the capabilities of terrestrial Radio Access Networks. Drones fitted with communication and relay modules can act as Drone Cells. The multi Drone Cell placement problem is solved using an adapted Dispersive Flies Optimization alongside with other meta-heuristic algorithms such as Particle Swarm Optimization and Differential Evolution. A home-brewed simulator has been used to test the effectiveness of the different implemented algorithms. Specific environment respective parameter tuning have been explored to better highlight possible advantages of one algorithm over other in any particular environment. Algorithmic diversity has been explored, leading to several modifications and improvements in the implemented models. The results show that using tuned parameters there is a performance uplift in coverage probability when compared to the default meta-heuristic parameters while still remaining within the constraints implied by the problem’s requirements and resource limitation. The paper concludes by offering a study between multiple meta-heuristic approaches and comparison between the methods, investigating the impact of parameter-tuning as well as analyzing the impact of intermittent restart for algorithms persistent diversity.
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
