The challenging problem of non-line-of-sight (NLOS) localization is critical for many wireless networking applications. The lack of available datasets has made NLOS localization difficult to tackle with ML-driven methods, but recent developments in synthetic dataset generation have provided new opportunities for research. This paper explores three different input representations: (i) single wireless radio path features, (ii) wireless radio link features (multi-path), and (iii) image-based representations. Inspired by the two latter new representations, we design two convolutional neural networks (CNNs) and we demonstrate that, although not significantly improving the NLOS localization performance, they are able to support richer prediction outputs, thus allowing deeper analysis of the predictions. In particular, the richer outputs enable reliable identification of non-trustworthy predictions and support the prediction of the top-K candidate locations for a given instance. We also measure how the availability of various features (such as angles of signal departure and arrival) affects the model's performance, providing insights about the types of data that should be collected for enhanced NLOS localization. Our insights motivate future work on building more efficient neural architectures and input representations for improved NLOS localization performance, along with additional useful application features.

翻译：非视距（NLOS）定位这一具有挑战性的问题对许多无线网络应用至关重要。数据集的缺乏使得基于机器学习的方法难以应对NLOS定位问题，但近期合成数据集生成技术的发展为研究提供了新机遇。本文探索了三种不同的输入表示：(i) 单条无线路径特征，(ii) 无线链路特征（多路径），以及(iii) 基于图像的表示。受后两种新表示的启发，我们设计了两种卷积神经网络（CNN），并证明尽管它们未能显著提升NLOS定位性能，但能够支持更丰富的预测输出，从而实现对预测结果的深入分析。具体而言，更丰富的输出能够可靠识别不可信的预测，并支持针对给定实例预测前K个候选位置。我们还测量了不同特征（如信号出发角和到达角）的可用性对模型性能的影响，为增强NLOS定位应采集的数据类型提供了见解。我们的发现为未来构建更高效的神经架构和输入表示以提升NLOS定位性能，以及开发更多实用应用功能指明了方向。