Deep Learning Based Localization and HO Optimization in 5G NR Networks

In the emerging 5G radio networks, beamforming-capable nodes are able to densely cover narrow areas with a high-quality signal. Such systems require high-level handover management system to proactively react to upcoming changes in signal quality, while restricting common issues such as ping-ponging or fast-shadowing of the signal. The utilization of deep learning in such a system allows for dynamic optimization of the system policies, based directly on the past behavior of the users and their channel responses. Our approach on handover optimization is purely non-deterministic, proving the idea that a self-learning network is able to efficiently manage user mobility in dense network scenario. The proposed network consists of feature extractors and dense layers. The model is trained in two stages, first serves as an initial weight setting in supervised fashion based on 3GPP model. The second stage is an optimization problem to reduce the number of unnecessary handovers while sustaining a high-quality connection. The model is also trained to predict the user location information as the second output. The presented results show that the number of handovers can be significantly reduced without decreasing the throughput of the system. The predicted location of the user has meter-level accuracy.