In the advent of next-generation wireless communication, millimeter-wave (mmWave) and terahertz (THz) technologies are pivotal for their high data rate capabilities. However, their reliance on large antenna arrays and narrow directive beams for ensuring adequate receive signal power introduces significant beam training overheads. This becomes particularly challenging in supporting highly-mobile applications such as drone communication, where the dynamic nature of drones demands frequent beam alignment to maintain connectivity. Addressing this critical bottleneck, our paper introduces a novel machine learning-based framework that leverages multi-modal sensory data, including visual and positional information, to expedite and refine mmWave/THz beam prediction. Unlike conventional approaches that solely depend on exhaustive beam training methods, our solution incorporates additional layers of contextual data to accurately predict beam directions, significantly mitigating the training overhead. Additionally, our framework is capable of predicting future beam alignments ahead of time. This feature enhances the system's responsiveness and reliability by addressing the challenges posed by the drones' mobility and the computational delays encountered in real-time processing. This capability for advanced beam tracking asserts a critical advancement in maintaining seamless connectivity for highly-mobile drones. We validate our approach through comprehensive evaluations on a unique, real-world mmWave drone communication dataset, which integrates concurrent camera visuals, practical GPS coordinates, and mmWave beam training data...

翻译：随着下一代无线通信时代的到来，毫米波（mmWave）和太赫兹（THz）技术因其高数据速率能力而至关重要。然而，这些技术依赖大规模天线阵列和窄定向波束来确保足够的接收信号功率，这引入了显著的波束训练开销。在支持无人机通信等高移动性应用时，这一问题变得尤为突出，因为无人机的动态特性要求频繁的波束对准以维持连接。为解决这一关键瓶颈，本文提出了一种新颖的基于机器学习的框架，该框架利用包括视觉和位置信息在内的多模态感知数据，以加速和优化毫米波/太赫兹波束预测。与仅依赖穷举式波束训练方法的传统方案不同，我们的解决方案整合了额外的上下文数据层，以准确预测波束方向，从而显著降低训练开销。此外，我们的框架能够提前预测未来的波束对准。这一特性通过应对无人机移动性带来的挑战以及实时处理中遇到的计算延迟，增强了系统的响应能力和可靠性。这种先进的波束跟踪能力对于维持高移动性无人机的无缝连接而言是一项关键进展。我们通过在一个独特的、真实世界的毫米波无人机通信数据集上进行全面评估来验证我们的方法，该数据集集成了同步的摄像机视觉数据、实际的GPS坐标以及毫米波波束训练数据...