# Machine Learning For In-Region Location Verification In Wireless Networks

## TL;DR

This paper explores machine learning techniques, including neural networks and support vector machines, for verifying if a user is inside a specific region in wireless networks, demonstrating their near-optimality and advantages over traditional methods.

## Contribution

It shows that ML solutions like NNs and SVMs are Neyman-Pearson optimal at convergence and outperform classical tests with finite data, also analyzing one-class classifiers for challenging scenarios.

## Key findings

- ML solutions are Neyman-Pearson optimal at convergence.
- ML methods outperform traditional channel estimation-based tests with limited data.
- Auto-encoders and one-class SVMs are effective for in-region verification when channel features are hard to estimate.

## Abstract

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) and typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets . Indeed, for finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoders NNs and one-class SVMs, which however are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical results support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading.