Low-Latency Communication with Drone-Assisted 5G Networks

ABSTRACTBackground: Unmanned Aerial Vehicles (UAVs) utilizing and active interface with 5G networks has become the new frontier to tackling problems of latency and energy efficiency, interference, and resource management. Although prior researches explained the benefits of UAV integrated networks; o...

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Veröffentlicht in:Pizhūhishnāmah-i pardāzish va mudiriyyat-i iṭṭilāʻāt (Online) Jg. 40; H. ویژه نامه انگلیسی 4; S. 765 - 796
Hauptverfasser: Hamid Alshareefi, Adil Abbas Majeed, Ertegin Baigashkaev, Basma Mohammed Khaleel, Ola Janan
Format: Journal Article
Sprache:Englisch
Persisch
Veröffentlicht: Iranian Research Institute for Information and Technology 01.07.2025
Schlagworte:
5g networks
energy efficiency
genetic algorithm (ga)
keywords: uavs
latency reduction
optimization algorithms
particle swarm optimization (pso)
signal-to-interference-plus-noise ratio (sinr)
task scheduling
the multi-objective evolutionary algorithm (moea)
ISSN:2251-8223, 2251-8231
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Zusammenfassung:ABSTRACTBackground: Unmanned Aerial Vehicles (UAVs) utilizing and active interface with 5G networks has become the new frontier to tackling problems of latency and energy efficiency, interference, and resource management. Although prior researches explained the benefits of UAV integrated networks; overall assessment of various parameters and cases is still scarce.Objective: The article seeks to assess the performance of UAV integrated 5G network in terms of latency, power, signal quality, task coordination and coverage optimization and to ascertain the efficiency of optimization algorithms in the improvement of the integrated 5G network.Methods: Emulations were done in MATLAB and NS3 platforms in urban / suburban / emergency call settings. Latency, power consumption, SINR, and completion time were the performance indicator chosen in the paper. Optimization algorithms: Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), and the Multi-Objective Evolutionary Algorithm (MOEA) is evaluated in terms of Convergence time and Solution quality.Results: UAV-aided networks showed 36.7% and 29.2 % improvement in latency and energy consumption, while 33.6 % enhancement in SINR. MOEA offered the best results with 98.3% solution quality, and the PSO being the most convergence oriented. Minor deviations between simulation and real results highlight the need for adaptive mechanisms.Conclusion: The results presented focus on the enough potential of UAV-assisted 5G networks and their potential influence on improving performances in case of different criteria. Further research should focus on successfully implementing and deploying the proposed solutions and broadening the context of study to include 6G technologies.
ISSN:2251-8223
2251-8231
DOI:10.22034/jipm.2025.728318