ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation
This work addresses NLOS localization for wireless networking applications, but it is incremental as it builds on existing synthetic datasets and does not achieve major performance gains.
The paper tackled the problem of non-line-of-sight (NLOS) localization in wireless networking by exploring three input representations and designing two CNNs, resulting in models that support richer prediction outputs like identifying non-trustworthy predictions and predicting top-K candidate locations, though without significant performance improvement.
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.