论文标题
高准确的到达时间估计，并为图像Inforet应用程序提供精细元素的功能
High Accurate Time-of-Arrival Estimation with Fine-Grained Feature Generation for Internet-of-Things Applications
论文作者
论文摘要
常规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。但是，在这封信中，我们建议从基于完整的频段和资源块（RB）的参考信号中生成细粒度的特征，并相应地计算互相关以改善观察分辨率以及TOA估计结果。使用类似频谱图的互相关特征图，我们将机器学习技术应用于脱钩的功能提取和拟合，以了解时间和频域中的变化，并将功能直接投影到TOA结果中。通过数值示例，我们表明，在静态传播环境中提出的高精确TOA估计可以在静态传播环境中至少取得51％的均方根误差（RMSE）改善，并且与现有的音乐和Esprit Algorith相比，多路径淡化环境的中值估计误差38 ns toa估计估计误差，这是相当于36％和25％的即时弹性。
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.
