8 research outputs found
Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach
Global Navigation Satellite Systems typically perform poorly in urban
environments, where the likelihood of line-of-sight conditions between the
devices and the satellites is low, and thus alternative localization methods
are required for good accuracy. We present LocUNet: A convolutional, end-to-end
trained neural network for the localization task, able to estimate the position
of a user from the received signal strength (RSS) from a small number of Base
Stations (BSs). In the proposed method, the user to be localized simply reports
the measured RSS to a central processing unit, which may be located in the
cloud. Using estimations of pathloss radio maps of the BSs and the RSS
measurements, LocUNet can localize users with state-of-the-art accuracy and
enjoys high robustness to inaccuracies in the estimations of radio maps. The
proposed method does not require pre-sampling of new environments and is
suitable for real-time applications. Moreover, two novel datasets that allow
for numerical evaluations of RSS and ToA methods in realistic urban
environments are presented and made publicly available for the research
community. By using these datasets, we also provide a fair comparison of
state-of-the-art RSS and ToA-based methods in the dense urban scenario and show
numerically that LocUNet outperforms all the compared methods.Comment: Submitted to IEEE Transactions on Wireless Communication
Dataset of Pathloss and ToA Radio Maps With Localization Application
In this article, we present a collection of radio map datasets in dense urban
setting, which we generated and made publicly available. The datasets include
simulated pathloss/received signal strength (RSS) and time of arrival (ToA)
radio maps over a large collection of realistic dense urban setting in real
city maps. The two main applications of the presented dataset are 1) learning
methods that predict the pathloss from input city maps (namely, deep
learning-based simulations), and, 2) wireless localization. The fact that the
RSS and ToA maps are computed by the same simulations over the same city maps
allows for a fair comparison of the RSS and ToA-based localization methods