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<p>Unmanned aerial vehicles (UAVs) and Terahertz (THz) communication are pivotal technologies for sixth-generation (6&#xa0;G) wireless networks. In this work, UAVs act as flying base stations (BSs) to serve groups of terrestrial mobile users in multi-UAV enabled non-orthogonal multiple access (NOMA) wireless networks operating at THz frequencies. However, the highly uncertain and dynamic nature of THz channels presents a significant challenge for efficient UAV path planning. To address this, we aim to maximize the energy efficiency and coverage probability in downlink communication. This is achieved by jointly optimizing the path of fixed-wing UAVs and the users’ power allocation, while considering various constraints on UAV mobility. This joint energy efficient and coverage aware UAV path planning (EECAPP) problem in UAV-THz networks is largely unexplored in the literature and, thus, is the focus of this research. The formulated EECAPP multi-objective optimization problem is mixed integer non-convex, which is generally NP-hard. Therefore, we propose an improved multi-objective grey wolf optimizer and differential evolution (I-MOGWODE) algorithm. Simulation results show that the proposed I-MOGWODE algorithm achieves the best trade-off between energy efficiency and coverage probability, outperforming other meta-heuristics algorithms. It delivers up to 15% higher energy efficiency and 3% higher coverage probability compared to the best-performing baseline, demonstrating its effectiveness in optimizing UAV trajectories for THz-NOMA networks.</p>

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A multi-objective optimization approach for path planning of fixed-wing UAVs in NOMA-THz networks

  • 期刊论文

  • 发表日期：

    2025年7月29日

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  • Aishwarya Gupta,
  • Aditya Trivedi,
  • Binod Prasad

摘要

Unmanned aerial vehicles (UAVs) and Terahertz (THz) communication are pivotal technologies for sixth-generation (6 G) wireless networks. In this work, UAVs act as flying base stations (BSs) to serve groups of terrestrial mobile users in multi-UAV enabled non-orthogonal multiple access (NOMA) wireless networks operating at THz frequencies. However, the highly uncertain and dynamic nature of THz channels presents a significant challenge for efficient UAV path planning. To address this, we aim to maximize the energy efficiency and coverage probability in downlink communication. This is achieved by jointly optimizing the path of fixed-wing UAVs and the users’ power allocation, while considering various constraints on UAV mobility. This joint energy efficient and coverage aware UAV path planning (EECAPP) problem in UAV-THz networks is largely unexplored in the literature and, thus, is the focus of this research. The formulated EECAPP multi-objective optimization problem is mixed integer non-convex, which is generally NP-hard. Therefore, we propose an improved multi-objective grey wolf optimizer and differential evolution (I-MOGWODE) algorithm. Simulation results show that the proposed I-MOGWODE algorithm achieves the best trade-off between energy efficiency and coverage probability, outperforming other meta-heuristics algorithms. It delivers up to 15% higher energy efficiency and 3% higher coverage probability compared to the best-performing baseline, demonstrating its effectiveness in optimizing UAV trajectories for THz-NOMA networks.