论文标题
位置辅助光束预测在现实世界中：GPS位置的实际有用？
Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?
论文作者
论文摘要
毫米波（mmwave）通信系统依靠狭窄的光束来实现足够的接收信号功率。调整这些梁通常与大型训练开销有关，这对于高度移动的应用特别重要。直观地，由于最佳光束选择可以受益于通信终端位置的知识，因此人们对利用位置数据的兴趣越来越多，以减少MMWave Beam预测中的开销。但是，先前的工作仅使用通常不能准确代表现实世界测量值的合成数据研究了这个问题。在本文中，我们使用现实世界中的大规模数据集研究了与位置辅助的光束预测，以洞悉准确地可以在实践中节省多少开销。此外，我们分析了哪种机器学习算法的性能最佳，哪些因素降低了实际数据中的推理性能以及哪些机器学习指标在捕获实际的通信系统性能方面更有意义。
Millimeter-wave (mmWave) communication systems rely on narrow beams for achieving sufficient receive signal power. Adjusting these beams is typically associated with large training overhead, which becomes particularly critical for highly-mobile applications. Intuitively, since optimal beam selection can benefit from the knowledge of the positions of communication terminals, there has been increasing interest in leveraging position data to reduce the overhead in mmWave beam prediction. Prior work, however, studied this problem using only synthetic data that generally does not accurately represent real-world measurements. In this paper, we investigate position-aided beam prediction using a real-world large-scale dataset to derive insights into precisely how much overhead can be saved in practice. Furthermore, we analyze which machine learning algorithms perform best, what factors degrade inference performance in real data, and which machine learning metrics are more meaningful in capturing the actual communication system performance.
