Hybrid Network–Spatial Clustering for Optimizing 5G Mobile Networks

5G is the new generation of 3GPP-based cellular communications that provides remarkable connectivity capabilities and extreme network performance to mobile network operators and cellular users worldwide. The rollout process of a new capacity layer (cell) on top of the existing previous cellular technologies is a complex process that requires time and manual effort from radio planning- engineering teams and parameter optimization teams. When it comes to optimum configuration of the 5G gNB cell parameters, the maximization of achieved coverage (RSRP) and quality (SINR) of the served mobile terminals are of high importance for achieving the very high data transmission rates expected in 5G. This process strongly relies on network measurements that can be even more insightful when mobile terminal localization information is present. This information can be gen- erated by modern algorithmic techniques that act on the cellular network signaling measurements. Configuration algorithms can then use these measurements combined with location information to optimize various cell deployment parameters such as cell azimuth. Furthermore, data-driven approaches are shown in the literature to outperform traditional, model-based algorithms as they can automate the optimization of parameters while specializing in the characteristics of each in- dividual geographical zone. In the context of the above, in this paper, we tested the automated network reconfiguration schemes based on unsupervised learning and applied statistics for cell azimuth steering. We compared network metric clustering and geospatial clustering to be used as our baseline algorithms that are based on K-means with the proposed scheme—hybrid network and spatial clustering based on hierarchical DBSCAN. Each of these algorithms used data generated by an initial scenario to produce cell re-configuration actions and their performance was then evalu- ated on a validated simulation platform to capture the impact of each set of gNB reconfiguration actions. Our performance evaluation methodology was based on statistical distribution analysis for RSRP and SINR metrics for the reference scenario as well as for each reconfiguration scheme. It is shown that while both baseline algorithms improved the overall performance of the network, the proposed hybrid network–spatial scheme greatly outperformed them in all statistical criteria that were evaluated, making it a better candidate for the optimization of 5G capacity layers in modern urban environments.