Open Access

ARTICLE

Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

Mohamed Amine Ouamri^1,2, Reem Alkanhel^3,*, Daljeet Singh⁴, El-sayed M. El-kenaway⁵, Sherif S. M. Ghoneim⁶

1 University Grenoble Alpes, CNRS, Grenoble INP, LIG, DRAKKAR Teams, 38000, Grenoble, France
2 Laboratoire d’informatique Médical, Université de Bejaia, Targa Ouzemour, Q22R+475, Algeria
3 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Research and Development, Centre for Space Research, School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, 144411, India
5 Department of Communication and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt
6 Electrical Engineering Department, College of Engineering, Taif University, P. O. BOX 11099, Taif, 21944, Saudi Arabia

* Corresponding Author: Reem Alkanhel. Email:

Computer Systems Science and Engineering 2023, 46(1), 73-92. https://doi.org/10.32604/csse.2023.034461

Received 18 July 2022; Accepted 22 September 2022; Issue published 20 January 2023

Abstract

Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive for dense cellular networks. Therefore, artificial intelligence algorithms are used to improve traditional RA mechanisms. Deep learning is a promising tool for addressing resource management problems in wireless communication. In this study, we investigate a double deep Q-network-based RA framework that maximizes energy efficiency (EE) and total network throughput in unmanned aerial vehicle (UAV)-assisted terrestrial networks. Specifically, the system is studied under the constraints of interference. However, the optimization problem is formulated as a mixed integer nonlinear program. Within this framework, we evaluated the effect of height and the number of UAVs on EE and throughput. Then, in accordance with the experimental results, we compare the proposed algorithm with several artificial intelligence methods. Simulation results indicate that the proposed approach can increase EE with a considerable throughput.

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Keywords

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