A Machine Learning based Accurate Localization Technique for 5G Networks

To cater the needs of network scalability and improved performance, 5G networks are set to achieve accurate localization in Indoor/Outdoor environment. This capability can be imparted in the network by training it to behave like a Real Dynamic Network (RDN). The proposed Accurate localization algorithm enable network nodes with self learning capability based on local observations. The decision making of the network is clearly autonomous and due to its self-learning capability, it behaves like a Heterogeneous network. With Ultra-Wide Band communication, the following measurements include Time of Arrival (TOA), Channel State Information (CSI) and Time Difference of Arrival (TDOA) are calculated for the network to justify the accuracy of the proposed algorithm. The Q learning model enhances the decision-making capability of nodes and base stations, which in turn enhance the localization of the proposed network. Simulation results prove that the Q learning model outperforms conventional approaches in terms of matching the performance requirements of 5G networks.