Hybrid Network–Spatial Clustering for Optimizing 5G Mobile Networks
نویسندگان
چکیده
5G is the new generation of 3GPP-based cellular communications that provides remarkable connectivity capabilities and extreme network performance to mobile operators users worldwide. The rollout process a capacity layer (cell) on top existing previous technologies complex requires time manual effort from radio planning-engineering teams parameter optimization teams. When it comes optimum configuration gNB cell parameters, maximization achieved coverage (RSRP) quality (SINR) served terminals are high importance for achieving very data transmission rates expected in 5G. This strongly relies measurements can be even more insightful when terminal localization information present. generated by modern algorithmic techniques act signaling measurements. Configuration algorithms then use these combined with location optimize various deployment parameters such as azimuth. Furthermore, data-driven approaches shown literature outperform traditional, model-based they automate while specializing characteristics each individual geographical zone. In context above, this paper, we tested automated reconfiguration schemes based unsupervised learning applied statistics azimuth steering. We compared metric clustering geospatial used our baseline K-means proposed scheme—hybrid spatial hierarchical DBSCAN. Each an initial scenario produce re-configuration actions their was evaluated validated simulation platform capture impact set actions. Our evaluation methodology statistical distribution analysis RSRP SINR metrics reference well scheme. It both improved overall network, hybrid network–spatial scheme greatly outperformed them all criteria were evaluated, making better candidate layers urban environments.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app12031203