Machine Learning-Enabled Localization in 5G using LIDAR and RSS Data

Demand for localization has been growing due to the increase in location-based services and high bandwidth applications requiring precise localization of users to improve resource management and beam forming. Outdoor localization has been traditionally done through Global Positioning System (GPS), however its performance degrades in urban settings due to obstruction and multi-path effects, creating the need for better localization techniques. We propose a technique using a cascaded approach composed of image classification and regional convolutional neural networks (CNN s) using LIDAR and received signal strength (RSS) data to predict the location of moving vehicles outdoors. We use simulated data in the mm Wave band that takes place in the neighbourhood of Rosslyn in Arlington, Virginia. Our results show an improvement in localization accuracy as a result of the hierarchical architecture, with a mean absolute error (MAE) of 6.55m for the proposed technique in comparison to a MAE of 9.82m using one CNN.