The distinction between line-of-sight (LOS) and non-line-of-sight (NLOS) channels is important for location awareness related technologies and wireless channel modeling. So far, most of the existing methods identify the LOS and NLOS channels based on the characteristics of radio propagation, e.g., using the Ricean K factor. However, the Ricean K factor is sensitive to the propagation environment, and it is thus difficult to find a proper threshold for NLOS identification. In this letter, we propose a novel NLOS identification method based on the convolutional neural network (CNN). Evaluated by channel measurement data, the proposed algorithm achieves better performance compared with the existing conventional method. Firstly, the CNN network is trained by using the pre-labeled LOS and NLOS data collected from channel measurements. The network parameters are set based on the feedback of training. Then, the method is validated by using different datasets. Compared with the Ricean K factor based identification method, the accuracy of which is 0.86, the proposed method shows higher accuracy of 0.99 for the NLOS channel identification.

中文翻译：

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基于卷积神经网络的信道非视线识别

视距 (LOS) 和非视距 (NLOS) 信道之间的区别对于位置感知相关技术和无线信道建模非常重要。到目前为止，大多数现有方法基于无线电传播的特性来识别LOS和NLOS信道，例如使用Ricean K因子。然而，Ricean K 因子对传播环境很敏感，因此很难找到合适的 NLOS 识别阈值。在这封信中，我们提出了一种基于卷积神经网络 (CNN) 的新型 NLOS 识别方法。通过信道测量数据评估，所提出的算法与现有的传统方法相比具有更好的性能。首先，使用从信道测量中收集的预先标记的 LOS 和 NLOS 数据训练 CNN 网络。根据训练的反馈设置网络参数。然后，通过使用不同的数据集来验证该方法。与基于 Ricean K 因子的识别方法相比，该方法的准确率为 0.86，该方法对 NLOS 信道识别的准确率为 0.99。