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
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-08, Vol.23 (16), p.7083
Main Authors: Alkanhel, Reem, Rafiq, Ahsan, Mokrov, Evgeny, Khakimov, Abdukodir, Muthanna, Mohammed Saleh Ali, Muthanna, Ammar
Format: Article
Language:English
Subjects:
Algorithms
deep learning
Design
Drone aircraft
Drones
Electric power transmission
Energy consumption
Energy efficiency
Equipment and supplies
Markov analysis
Optimization techniques
resource allocation
slime mould algorithm
Tang Taizong, Emperor of China
Teaching
Telecommunication systems
Unmanned aerial vehicles
Wireless communications
Wireless networks
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Summary: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. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23167083