This paper presents a novel and efficient wireless channel estimation scheme based on a tapped delay line (TDL) model of wireless signal propagation, where a data-driven machine learning approach is used to estimate the path delays and gains. The key motivation for our novel channel estimation model is to gain environment awareness, i.e., detecting changes in path delays and gains related to interesting objects and events in the field. The estimated channel state provides a more detailed measure to sense the field than the single-tap channel state indicator (CSI) in current OFDM systems. Advantages of this approach also include low computation time and training data requirements, making it suitable for environment awareness applications. We evaluate this model's performance using Matlab's ray-tracing tool under static and dynamic conditions for increased realism instead of the standard evaluation approaches that rely on classical statistical channel models. Our results show that our TDL-based model can accurately estimate the path delays and associated gains for a broad-range of locations and operating conditions. Root-mean-square estimation error was less than $10^{-4}$, or $-40$dB, for SNR $\geq 60$dB in all of our experiments. Our results show that interference of a flying drone on signal multipaths, in a preliminary experiment, can be detected in estimated channel states which, otherwise, remains obscured in conventional CSI.

翻译：本文提出了一种基于无线信号传播的抽头延迟线（TDL）模型的新型高效无线信道估计方案，该方案采用数据驱动的机器学习方法来估计路径延迟和增益。我们提出这一新型信道估计模型的关键动机在于获取环境感知能力，即检测与现场感兴趣物体和事件相关的路径延迟和增益的变化。与当前OFDM系统中的单抽头信道状态指示器（CSI）相比，估计的信道状态为感知现场环境提供了更精细的度量。该方法的优势还包括计算时间短、训练数据需求少，使其适用于环境感知应用。我们使用Matlab的射线追踪工具在静态和动态条件下评估该模型的性能，以增强现实性，而非依赖经典统计信道模型的标准评估方法。我们的结果表明，基于TDL的模型能够准确估计各种位置和操作条件下的路径延迟及相关增益。在所有实验中，当信噪比（SNR）≥ 60dB时，均方根估计误差小于$10^{-4}$，即$-40$dB。我们的结果还显示，在一项初步实验中，飞行无人机对信号多径的干扰可以在估计的信道状态中被检测到，而这在传统的CSI中通常是无法察觉的。