Machine learning based localization in 5G.

Summary Localization is the process of estimating the position of an entity in a local or global coordinate system. Localization applications are widely distributed in different contexts. In events, tracking participants can save lives during crises. In healthcare, elderly people can be tracked to meet their needs in critical situations such as falls. In warehouses, robots transferring products from one place to another require precise knowledge of its position, the position of the products as well as of other robots. In an industrial context, localization is essential to realize automated processes that are flexible enough to be reconfigured to various missions. Localization is considered a topic of great interest in both industry and academia, especially with the advent of 5G with its "Enhanced Mobile Broadband (eMBB)" which is expected to reach 10 Gbps, "Ultra-Reliable Low-Latency Communication (URLLC)" which is less than one millisecond and "massive Machine-Type Communication (mMTC)" allowing to connect about 1 million devices per kilometer.In this work, we focus on two main types of localization. distance-based localization between devices and fingerprint-based localization. In distance-based localization, a network of devices with a maximum communication distance estimates distance values from their neighbors. These distances along with the knowledge of the positions of some nodes are used to locate other nodes in the network using a triangulation-based solution. The proposed method is able to locate about 90% of the nodes in a network with an average degree of 10.In fingerprint-based localization, the responses of wireless channels are used to estimate the position of a transmitter communicating with a MIMO antenna. In this work, we apply classical learning techniques (K-nearest neighbors) and deep learning techniques (Multi-Layer Perceptron Neural Network and Convolutional Neural Networks) to localize a transmitter in indoor and outdoor settings. Our work won the first prize in the positioning competition prepared by IEEE Communication Theory Workshop among 8 teams from highly reputable universities around the world by obtaining a mean square error of 2.3 cm.