Electronic and Cyber Defense

Electronic and Cyber Defense

3D UAV deployment and resource allocation in hybrid NOMA-OMA systems

Document Type : Original Article

Authors
naser neda 1
Zahra Hasani 2
Mohamad Hasan Majidi 3
saeed Khorashadizadeh 2
1 Faculty of electrical and computer engineering- University of Briand Birjand - Iran
2 Faculty of Electrical and computer engineering, University of Birjand Birjand - Iran
3 Faculty of electrical and computer engineering, University of Birjand Birjand - Iran
Abstract
ABSTRACT

In recent years, Unmanned Aerial Vehicle technology (UAV) has gained significant attention in modern telecommunications networks due to the various capabilities it offers. This paper investigates the joint optimization of the three-dimensional locations of UAV, user grouping on the ground, and power allocation for users in the uplink, with the aim of maximizing energy efficiency. At the same time, the users' quality of service (QoS) requirements are also considered. In this study, UAV is utilized as mobile base stations (BS), and information transfer occurs in the physical layer over non-orthogonal multiple access (NOMA).The main idea of this research is to present a communication system that: 1) combines the advantages of UAV communications with the capabilities provided by non-orthogonal multiple access, 2) maximizes energy efficiency, and 3) employs a Q-learning algorithm to solve the optimization problem. The performance analysis of the proposed framework demonstrates the effectivenessof optimal resource allocation approach. Numericalresults indicate that the proposed scheme, which includes optimal UAV deployment, user grouping, and optimal power allocation,significantly improves user energy efficiency compared to random grouping and uniform power allocation methods. Furthermore, results show that the Q-learning algorithm quickly converges to the optimal solution after the training phase.
Keywords
UAV
NOMA
QL
Resource Allocation
Energy Efficiency
Subjects

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  • Receive Date 28 February 2026
  • Revise Date 29 April 2026
  • Accept Date 14 June 2026
  • Publish Date 02 July 2026