Real-time Localization Using Radio Maps
This addresses the problem of reliable localization for users in urban environments with poor satellite signals, though it appears incremental as it builds on existing pathloss simulation methods.
The paper tackles localization in dense urban cellular networks where GNSS fails, by using pathloss approximations from a deep learning simulator (RadioUNet) and reported signal strengths to achieve very accurate user location estimation.
This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite System typically performs poorly in urban environments when there is no line-of-sight between the devices and the satellites, and thus alternative localization methods are often required. We present a simple yet effective method for localization based on pathloss. In our approach, the user to be localized reports the received signal strength from a set of base stations with known locations. For each base station we have a good approximation of the pathloss at each location in the map, provided by RadioUNet, an efficient deep learning-based simulator of pathloss functions in urban environment, akin to ray-tracing. Using the approximations of the pathloss functions of all base stations and the reported signal strengths, we are able to extract a very accurate approximation of the location of the user.