We consider the problem of proactive handoff and beam selection in Terahertz (THz) drone communication networks assisted with reconfigurable intelligent surfaces (RIS). Drones have emerged as critical assets for next-generation wireless networks to provide seamless connectivity and extend the coverage, and can largely benefit from operating in the THz band to achieve high data rates (such as considered for 6G). However, THz communications are highly susceptible to channel impairments and blockage effects that become extra challenging when accounting for drone mobility. RISs offer flexibility to extend coverage by adapting to channel dynamics. To integrate RISs into THz drone communications, we propose a novel deep learning solution based on a recurrent neural network, namely the Gated Recurrent Unit (GRU), that proactively predicts the serving base station/RIS and the serving beam for each drone based on the prior observations of drone location/beam trajectories. This solution has the potential to extend the coverage of drones and enhance the reliability of next-generation wireless communications. Predicting future beams based on the drone beam/position trajectory significantly reduces the beam training overhead and its associated latency, and thus emerges as a viable solution to serve time-critical applications. Numerical results based on realistic 3D ray-tracing simulations show that the proposed deep learning solution is promising for future RIS-assisted THz networks by achieving near-optimal proactive hand-off performance and more than 90% accuracy for beam prediction.

中文翻译：

---

飞行智能表面的太赫兹无人机深度学习：波束和越区切换预测

我们考虑了在可重配置智能曲面（RIS）辅助下的太赫兹（THz）无人机通信网络中的主动切换和波束选择问题。无人机已成为下一代无线网络提供无缝连接和扩展覆盖范围的关键资产，并且可以从太赫兹频段的操作中获得较高的数据速率（例如6G），从而在很大程度上受益。但是，太赫兹通信极易受到信道损伤和阻塞影响的影响，这在考虑无人机移动性时变得尤为困难。RIS通过适应信道动态变化提供了扩展覆盖范围的灵活性。为了将RIS集成到太赫兹无人机通信中，我们提出了一种基于递归神经网络的新型深度学习解决方案，即门控递归单元（GRU），基于先前对无人机位置/波束轨迹的观察，可以主动预测每个无人机的服务基站/ RIS和服务波束。该解决方案有可能扩展无人机的覆盖范围并提高下一代无线通信的可靠性。基于无人机波束/位置轨迹预测未来波束会显着减少波束训练开销及其相关的等待时间，因此成为服务于时间紧迫应用的可行解决方案。基于逼真的3D射线跟踪模拟的数值结果表明，所提出的深度学习解决方案通过实现近乎最佳的主动切换性能和90％以上的波束预测准确度，对未来的RIS辅助THz网络很有希望。