LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning

TL;DR

LocUNet is a deep learning-based localization method that uses radio maps and RSS data to achieve accurate, real-time urban positioning without increasing device complexity, robust to radio map inaccuracies.

Contribution

The paper introduces LocUNet, a novel deep learning approach for urban localization using radio maps and RSS, enabling real-time, accurate positioning without extra device computation.

Findings

Achieves state-of-the-art localization accuracy in urban scenarios.

Robust to radio map inaccuracies and suitable for real-time applications.

Provides datasets for comparing RSS and ToA localization methods.

Abstract

This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban environments, where the likelihood of line-of-sight conditions is low, and thus alternative localization methods are required for good accuracy. We present LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs), which does not require any increase in computation complexity at the user devices with respect to the device standard operations, unlike methods that rely on time of arrival or angle of arrival information. In the proposed method, the user to be localized reports the RSS from BSs to a Central Processing Unit (CPU), which may be located in the cloud. Alternatively, the localization can be performed locally at the user. Using estimated pathloss radio maps of the BSs, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the radio maps. The proposed method does not require pre-sampling of the environment; and is suitable for real-time applications, thanks to the RadioUNet, a neural network-based radio map estimator. We also introduce two datasets that allow numerical comparisons of RSS and Time of Arrival (ToA) methods in realistic urban environments.