Conventional schemes often require extra reference signals or more complicated algorithms to improve the time-of-arrival (TOA) estimation accuracy. However, in this letter, we propose to generate fine-grained features from the full band and resource block (RB) based reference signals, and calculate the cross-correlations accordingly to improve the observation resolution as well as the TOA estimation results. Using the spectrogram-like cross-correlation feature map, we apply the machine learning technology with decoupled feature extraction and fitting to understand the variations in the time and frequency domains and project the features directly into TOA results. Through numerical examples, we show that the proposed high accurate TOA estimation with fine-grained feature generation can achieve at least 51% root mean square error (RMSE) improvement in the static propagation environments and 38 ns median TOA estimation errors for multipath fading environments, which is equivalently 36% and 25% improvement if compared with the existing MUSIC and ESPRIT algorithms, respectively.

中文翻译：

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用于物联网应用的具有细粒度特征生成的高精度到达时间估计

传统方案通常需要额外的参考信号或更复杂的算法来提高到达时间 (TOA) 估计精度。然而，在这封信中，我们建议从基于全频带和资源块 (RB) 的参考信号中生成细粒度特征，并相应地计算互相关以提高观测分辨率以及 TOA 估计结果。使用类似频谱图的互相关特征图，我们应用具有解耦特征提取和拟合的机器学习技术来了解时域和频域的变化，并将特征直接投影到 TOA 结果中。通过数值例子，