Intelligent framework for radio access network design


The evolution of 5G networks over the last few years has introduced a variety of technologies for more efficient radio access networks (RANs), which end up in ultra-dense heterogeneous infrastructure with deployments of high complexity. In this paper, we propose a new framework for RAN design in ultra-dense urban scenario based on the machine learning. The key idea of the proposed framework is to bring intelligent capabilities to the coverage planning problem for complex multi-tier scenarios, in order to achieve better network performance. We design our framework for small cells coverage optimization with 3D urban environment, macro cell locations, and realistic traffic statistics. Simulation results show that our proposed intelligent RAN framework significantly outperforms the conventional coverage design solutions, even after only a short learning time.

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This work was supported by the Slovak Research and Development Agency, Project Numbers APVV-15-0055 and APVV-18-0214, Scientific Grant Agency of the Ministry of Education, science, research and sport of the Slovak Republic under the Contract No. 1/0268/19 and by the European Intergovernmental Framework COST Action CA15140: Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice. This research was also supported by the Project No. 0117U007177 “Designing the methods of adaptive radio resource management in LTE-U mobile networks for 4G/5G development in Ukraine,” funded by Ukrainian government.

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Correspondence to Taras Maksymyuk.

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Maksymyuk, T., Šlapak, E., Bugár, G. et al. Intelligent framework for radio access network design. Wireless Netw 26, 759–774 (2020).

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  • RAN workflow
  • HetNets
  • Artificial intelligence
  • Self-supervised learning
  • Big data