Citation

Khan, Adil and Ahmad, Shabeer and Hayat, Babar and Liu, Weixing and Ullah, Yasir and Roslee, Mardeni (2025) Learning based Mobility Management in UAV Connected Software-Defined Heterogeneous Network. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

Heterogeneous Networks (HetNets) are deployed with cells of different sizes and coverage areas in mobile networks to provide high capacity, data rate, and services to ground users, posing a challenge in establishing connectivity for remotely flying unmanned aerial vehicles (UAVs). Due to differing mobility characterizations from ground users, the addition of UAV users connected to a HetNet adds mobility management (MM) challenges. These challenges maximize the number of handovers (HO) and degrade the performance of UAV users. This paper presents an intelligent Q-learning-based handover and mobility management (QHMM) approach that aims to resolve the challenges associated with MM and guarantee reliable and consistent communication for UAV users. Software-defined HetNet (SDHN) reduces the network complexity by separating the control plane. The proposed approach ensures enhanced connectivity and mobility support for UAV users by integrating Q learning and adjusting the antenna tilt (AT) angles of the Base station (BS) to select the optimal next cell for HO. The HO value is calculated based on multiple parameters, which include UAV direction, received signal strength (RSS), Communication distance, and dwell time. The results indicate that the proposed approach minimized the number of HO, outage probability, and maximized energy efficiency and user throughput compared to the state-of-the-art techniques.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Q-learning, Handover, Mobility management, and energy efficiency.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	19 Mar 2026 00:10
Last Modified:	19 Mar 2026 00:10
URII:	http://shdl.mmu.edu.my/id/eprint/15603

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