Citation

Ullah, Yasir and Roslee, Mardeni and Mitani, Sufian Mousa and Sheraz, Muhammad and Ali, Farman and Osman, Anwar Faizd and Jusoh, Mohamad Huzaimy and Sudhamani, Chilakala (2024) Reinforcement learning-based unmanned aerial vehicle trajectory planning for ground users’ mobility management in heterogeneous networks. Journal of King Saud University - Computer and Information Sciences, 36 (5). p. 102052. ISSN 1319-1578

Text 3.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets.

Item Type:	Article
Uncontrolled Keywords:	Mobility management, Handover failures, Heterogeneous networks, UAV-BS, UAV 3D trajectory, Reinforcement learning
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	30 May 2024 02:29
Last Modified:	30 May 2024 02:29
URII:	http://shdl.mmu.edu.my/id/eprint/12490

Downloads

Edit (login required)