A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning.

Autor: Seerangan K; Department of CSE (AI&ML), S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, 600077, India., Nandagopal M; Department of CSE, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India., Govindaraju T; Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, Tamil Nadu, 603203, India., Manogaran N; Department of CSE, S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, 600077, India., Balusamy B; Shiv Nadar (Institution of Eminence Deemed to Be University), Greater Noida, Uttar Pradesh, 201314, India., Selvarajan S; Department of Computer Science, Kebri Dehar University, 250, Kebri Dehar, Ethiopia. ShitharthS@kdu.edu.et.; School of Built Environment, Engineering and Computing, Leeds Beckett University, LS6 3QS, Leeds, UK. ShitharthS@kdu.edu.et.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Sep 27; Vol. 14 (1), pp. 22188. Date of Electronic Publication: 2024 Sep 27.
DOI: 10.1038/s41598-024-71621-x
Abstrakt: In the air-to-ground transmissions, the lifespan of the network is based on the "unmanned aerial vehicle's (UAV)" life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate's minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage. Because of their flexible deployment and the high maneuverability, the UAVs can be the best alternative for the situations where the "Internet of Things (IoT)" systems utilize more energy to attain the essential information rate, when they are far away from the terrestrial base station. Therefore, it is significant to win over the few troubles in the conventional UAV-aided efficiency approaches. Thus, this proposed work is aimed to design an innovative energy efficiency framework in the UAV-assisted network using a reinforcement learning mechanism. The energy efficiency optimization in the UAV offers better wireless coverage to the static and mobile ground user. Presently, reinforcement learning techniques effectively optimize the energy efficiency rate of the system by employing the 2D trajectory mechanism, which effectively removes the interference rate attained in the nearby UAV cells. The main objective of the recommended framework is to maximize the energy efficiency rate of the UAV network by performing the joint optimization using UAV 3D trajectory, with the energy utilized during interference accounting, and connected user counts. Hence, an efficient Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework is designed to provide a better energy efficiency rate to the UAV network. Moreover, the parameter of ADRL is tuned using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm. Various experimental observations are performed to observe the effectualness rate of the recommended energy efficiency model for UAV-based networks over the classical energy efficiency framework in UAV Networks.
(© 2024. The Author(s).)
Databáze: MEDLINE
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