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Updated: Sep 17, 2025

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Resource management for multi-drone communications in next-generation NOMA-enabled wireless networks.

Sajed Ahmad1, Syed Zain Ul Abideen2, Mian Muhammad Kamal3

  • 1College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China. sajedahmad86@gmail.com.

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Summary

This study introduces an optimization framework for Unmanned Aerial Drone (UAD) networks using Non-Orthogonal Multiple Access (NOMA). It enhances spectral efficiency and network performance in dynamic scenarios.

Keywords:
Flexible infrastructureNOMA (Non-Orthogonal Multiple Access)Network capacityNext-generation wireless networksUnmanned Aerial Drone (UAD) Communication

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Area of Science:

  • Wireless Communication Networks
  • Network Optimization
  • Aerial Communication Systems

Background:

  • Unmanned Aerial Drones (UADs) are crucial for next-generation wireless networks, providing flexible coverage for remote and emergency situations.
  • Challenges in UAD networks include high latency, low spectral efficiency, and fairness issues among multiple drones.
  • Existing solutions often struggle with the complexity of optimizing UAD-user associations and power allocation.

Purpose of the Study:

  • To develop an optimization framework for multi-UAD communication networks utilizing Non-Orthogonal Multiple Access (NOMA).
  • To address challenges of high latency, low spectral efficiency, and fairness in UAD networks.
  • To maximize spectral efficiency by optimizing ground user-to-UAD associations and drone power allocation.

Main Methods:

  • Developed a framework for optimizing multi-UAD communication networks based on NOMA.
  • Addressed the complex mixed-integer, nonconvex, and nonlinear optimization problem.
  • Implemented a two-stage optimization: first UAD-user associations, then NOMA power allocation for each user.

Main Results:

  • The proposed NOMA framework significantly outperforms traditional Orthogonal Multiple Access (OMA) methods.
  • Achieved improved spectral efficiency compared to benchmark NOMA techniques.
  • Demonstrated lower complexity and faster convergence in numerical simulations.

Conclusions:

  • The optimization framework effectively enhances spectral efficiency and overall performance in multi-UAD networks.
  • The proposed NOMA-based approach offers a superior solution compared to OMA and other NOMA techniques.
  • This framework provides a robust and efficient method for improving UAD network connectivity in dynamic environments.