利用变压器模型估算多径分量延迟

Jonathan Ott;Maximilian Stahlke;Tobias Feigl;Christopher Mutschler
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引用次数: 0

摘要

无线电传播中的多路径提供了重要的环境信息,可用于定位或信道同步定位和制图。与传统方法相比,这种方法只需较少的基础设施,就能实现精确、稳健的定位。一个关键因素是可靠、准确地提取多径成分(MPC)。然而,有限的带宽和信号衰减使得检测和确定单个信号分量的参数变得十分困难。在本文中,我们提出了基于变压器神经网络的多径延迟估计。与现有技术相比,我们隐含地估计了多路径延迟的数量,并在不使用计算密集型超分辨率技术的情况下实现了子样本精度。在不同带宽条件下,我们的方法在检测性能和准确性上都优于已知方法。我们的消融研究在模拟和真实数据集上显示了卓越的结果,并可推广到未知的无线电环境中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating Multipath Component Delays With Transformer Models
Multipath in radio propagation provides essential environmental information that is exploited for positioning or channel-simultaneous localization and mapping. This enables accurate and robust localization that requires less infrastructure than traditional methods. A key factor is the reliable and accurate extraction of multipath components (MPCs). However, limited bandwidth and signal fading make it difficult to detect and determine the parameters of the individual signal components. In this article, we propose multipath delay estimation based on a transformer neural network. In contrast to the state of the art, we implicitly estimate the number of MPCs and achieve subsample accuracy without using computationally intensive super-resolution techniques. Our approach outperforms known methods in detection performance and accuracy at different bandwidths. Our ablation study shows exceptional results on simulated and real datasets and generalizes to unknown radio environments.
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