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<p>In this paper, deploying multiple UAVs is proposed to provide high-data-rate wireless connectivity to ground vehicles using millimeter-wave (mmWave) links. However, mmWave beamforming and beam tracking present significant challenges in this high-mobility environment. Additionally, UAVs should adapt their trajectories with the vehicles’ ground paths to minimize frequent UAVs handovers while avoiding physical aerial collisions. To address these complexities, delay-Doppler (DD) channel parameters between UAVs and ground vehicles and among UAVs are exploited using orthogonal time frequency space (OTFS) modulation. The proposed approach enables the estimation of vehicle’s speed, its moving direction, and future positions, facilitating robust mmWave beam tracking and proactive UAV trajectory planning. The estimation of the DD channel parameters among UAVs enables the prediction of their inter-distances, which supports aerial collision avoidance while maintaining precise tracking of the ground vehicles. Numerical analyses validate the efficiency of the proposed DD-based framework in overcoming UAV-vehicular integration challenges, outperforming existing benchmarks.</p>

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A delay-Doppler approach for joint mmWave beamforming and proactive UAV trajectory planning in UAV-vehicular communications

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Ehab Mahmoud Mohamed,
  • Hany S. Hussein

摘要

In this paper, deploying multiple UAVs is proposed to provide high-data-rate wireless connectivity to ground vehicles using millimeter-wave (mmWave) links. However, mmWave beamforming and beam tracking present significant challenges in this high-mobility environment. Additionally, UAVs should adapt their trajectories with the vehicles’ ground paths to minimize frequent UAVs handovers while avoiding physical aerial collisions. To address these complexities, delay-Doppler (DD) channel parameters between UAVs and ground vehicles and among UAVs are exploited using orthogonal time frequency space (OTFS) modulation. The proposed approach enables the estimation of vehicle’s speed, its moving direction, and future positions, facilitating robust mmWave beam tracking and proactive UAV trajectory planning. The estimation of the DD channel parameters among UAVs enables the prediction of their inter-distances, which supports aerial collision avoidance while maintaining precise tracking of the ground vehicles. Numerical analyses validate the efficiency of the proposed DD-based framework in overcoming UAV-vehicular integration challenges, outperforming existing benchmarks.