With the advent of machine learning, there has been renewed interest in the problem of wireless channel estimation. This paper presents a novel low-complexity 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. Advantages of this approach include low computation time and training data requirements, as well as interpretability since the estimated model parameters and their variance provide comprehensive representation of the dynamic wireless multipath environment. 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 using 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 remained less than $10^{-4}$, or $-40$dB, for SNR $\geq 30$dB in all of our experiments. The key motivation for the 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 channel state with multipath delays and gains is a detailed measure to sense the field than the single-tap channel state indicator calculated in current OFDM systems.

翻译：随着机器学习的兴起，无线信道估计问题重新引起了研究兴趣。本文提出了一种基于无线信号传播抽头延迟线（TDL）模型的新型低复杂度信道估计方案，其中采用数据驱动机器学习方法估计路径时延和增益。该方法的优势包括计算时间和训练数据需求低，同时具有可解释性——因为估计的模型参数及其方差能够为动态无线多径环境提供全面表征。我们使用Matlab射线追踪工具在静态和动态条件下评估该模型性能以增强真实性，而非采用统计信道模型的标准评估方法。结果表明，基于TDL的模型能够准确估计广泛位置和运行条件下的路径时延及相关增益。在所有实验中，当信噪比（SNR）≥30dB时，均方根估计误差保持在$10^{-4}$（即-40dB）以下。该新型信道估计模型的核心动机在于实现环境感知能力，即检测与场景中感兴趣物体及事件相关的路径时延和增益变化。相比当前OFDM系统中计算的单信道状态指示，包含多径时延和增益的信道状态能作为更精细的场域感知度量。