Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks

Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks

Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs...

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Bibliographic Details
Main Authors: Reem Alkanhel, Ahsan Rafiq, Evgeny Mokrov, Abdukodir Khakimov, Mohammed Saleh Ali Muthanna, Ammar Muthanna
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Sensors
Subjects:
unmanned aerial vehicles
slime mould algorithm
resource allocation
deep learning
wireless networks
Online Access:https://www.mdpi.com/1424-8220/23/16/7083

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