Machine Learning Enabled-Localization in 5G and LTE Using Image Classification and Deep Learning

Abstract

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 it’s performance degrades in urban settings due to obstruction and multi-path effects, creating the need for better localization techniques. This thesis proposes a technique using a cascaded approach composed of image classification and deep learning using LIDAR or satellite images and Channel State In-formation (CSI) data from base stations to predict the location of moving vehicles and users outdoors. The algorithm’s performance is assessed using 3 different datasets. The first two use simulated data in the Milli-meter Wave (mmWave) band and lidar images that are collected from the neighbourhood of Rosslyn in Arlington, Virginia. The 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 Convolutional Neural Network (CNN). The third dataset uses measurements from an LTE mobile communication system along with satellite images that take place at the University of Denmark. The results achieve a MAE of 9.45 m fort he heirchichal approach in comparison to a MAE of 15.74 m for one Feed-Forward Neural Network (FFNN).