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<p>Conventional base stations (BS) often face limitations in providing consistent communication services, particularly in remote or congested areas. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution to assist BS in expanding coverage and improving service quality. However, the primary challenge with UAVs lies in their limited battery life, which restricts flight time and overall efficiency. To address this, non-orthogonal multiple access (NOMA) is a promising technique that can significantly improve the energy efficiency (EE) of the network. In this paper, we first formulate a joint resource optimization problem to maximize EE while ensuring quality of service (QoS) constraints. Then, a low-complexity heuristic solution is presented to address a nonlinear, nonconvex, and NP-hard problem by optimizing the key parameters, including user pairing (UP), UAV altitude (UA), and power allocation (PA). The simulation results show that our proposed solution outperforms benchmark schemes, achieving 23.07% higher EE than random pairing and 28.67% higher EE than the worst-case pairing scenario.</p>

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Maximizing energy efficiency through joint resource optimization in NOMA-assisted UAV-enabled next-generation wireless networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月30日

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  • Rahat Ullah,
  • Mubashar Sarfraz,
  • Muhammad Farhan Sohail,
  • Sheraz Alam,
  • Sajjad A. Ghauri,
  • Nauman Anwar Baig

摘要

Conventional base stations (BS) often face limitations in providing consistent communication services, particularly in remote or congested areas. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution to assist BS in expanding coverage and improving service quality. However, the primary challenge with UAVs lies in their limited battery life, which restricts flight time and overall efficiency. To address this, non-orthogonal multiple access (NOMA) is a promising technique that can significantly improve the energy efficiency (EE) of the network. In this paper, we first formulate a joint resource optimization problem to maximize EE while ensuring quality of service (QoS) constraints. Then, a low-complexity heuristic solution is presented to address a nonlinear, nonconvex, and NP-hard problem by optimizing the key parameters, including user pairing (UP), UAV altitude (UA), and power allocation (PA). The simulation results show that our proposed solution outperforms benchmark schemes, achieving 23.07% higher EE than random pairing and 28.67% higher EE than the worst-case pairing scenario.