No abstract

In the emerging 5G radio networks, beamformingcapable 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 pingponging 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.

Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy consumption. Device resources should be used appropriately, especially when employed for time-consuming and energy-intensive computations such as positioning or localization. However, reducing computational costs usually degrades the positioning methods. Therefore, the goal of this article is to propose and compare compression mechanisms of the fingerprinting datasets for energy-saving without losing relevant information, by using adaptive k-means clustering. As a result, we achieved a compression ratio of up to 15.97 with a small decrease (1%) in position error.