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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Hwang, I., Song, B., & Soliman, S. S. (2013). A holistic view on hyper-dense heterogeneous and small cell networks. IEEE Communications Magazine, 51(6), 20–27.
Collet, P., & Rennard, J.-P. (2008). Stochastic optimization algorithms. In: Intelligent information technologies: Concepts, methodologies, tools, and applications (pp. 1121–1137). IGI Global.
O’Leary, D. E. (2013). Artificial intelligence and big data. IEEE Intelligent Systems, 28(2), 96–99.
Moysen, J., & Giupponi, L. (2018). From 4G to 5G: Self-organized network management meets machine learning. Computer Communications, 129, 248–268.
Moysen, J., Giupponi, L., & Mangues-Bafalluy, J. (2016). On the potential of ensemble regression techniques for future mobile network planning. In IEEE Symposium on Computers and Communication (ISCC) (pp. 477–483). IEEE.
Popoola, S. I., Adetiba, E., Atayero, A. A., Faruk, N., & Calafate, C. T. (2018). Optimal model for path loss predictions using feed-forward neural networks. Cogent Engineering, 5(1), 1444345.
Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access, 6, 32 328–32 338.
Wang, X., Li, X., & Leung, V. C. (2015). Artificial intelligence-based techniques for emerging heterogeneous network: State of the arts, opportunities, and challenges. IEEE Access, 3, 1379–1391.
Gazda, J., Šlapak, E., Bugár, G., Horváth, D., Maksymyuk, T., & Jo, M. (2018). Unsupervised learning algorithm for intelligent coverage planning and performance optimization of multitier heterogeneous network. IEEE Access, 6, 39 807–39 819.
Gutierrez-Estevez, D. M., Gramaglia, M., De Domenico, A., Dandachi, G., Khatibi, S., Tsolkas, D., et al. (2019). Artificial intelligence for elastic management and orchestration of 5G networks. IEEE Wireless Communications, 26(5),134–141.
Wang, D., Song, B., Chen, D., & Du, X. (2019). Intelligent cognitive radio in 5G: AI-based hierarchical cognitive cellular networks. IEEE Wireless Communications, 26(3), 54–61.
Zhang, H., Ren, Y., Chen, K.-C., Hanzo, L., et al. (2019). Thirty years of machine learning: The road to pareto-optimal next-generation wireless networks. arXiv preprint arXiv:1902.01946.
Maksymyuk, T., Dumych, S., Brych, M., Satria, D., & Jo, M. (2017). An IoT based monitoring framework for software defined 5G mobile networks. In Proceedings of the 11th international conference on ubiquitous information management and communication (p. 105). ACM.
Heath, R. W., Kountouris, M., & Bai, T. (2013). Modeling heterogeneous network interference using Poisson point processes. IEEE Transactions on Signal Processing, 61(16), 4114–4126.
Peng, K., Leung, V. C., & Huang, Q. (2018). Clustering approach based on mini batch K-means for intrusion detection system over big data. IEEE Access, 6, 11 897–11 906.
Kriegel, H.-P., Schubert, E., & Zimek, A. (2017). The (black) art of runtime evaluation: Are we comparing algorithms or implementations? Knowledge and Information Systems, 52(2), 341–378.
Sculley, D. (2010). Web-scale K-means clustering. In Proceedings of the 19th international conference on World wide web (pp. 1177–1178). ACM.
Feizollah, A., Anuar, N. B., Salleh, R., & Amalina, F. (2014). Comparative study of K-means and mini batch K-means clustering algorithms in Android malware detection using network traffic analysis. In International Symposium on Biometrics and Security Technologies (ISBAST) (pp. 193–197). IEEE.
Dhillon, H. S., Ganti, R. K., & Andrews, J. G. (2011). A tractable framework for coverage and outage in heterogeneous cellular networks. In Information Theory and Applications Workshop (pp. 1–6). IEEE.
Alouini, M.-S., & Goldsmith, A. J. (1999). Area spectral efficiency of cellular mobile radio systems. IEEE Transactions on Vehicular Technology, 48(4), 1047–1066.
Jain, R., Durresi, A., & Babic, G. (1999). Throughput fairness index: An explanation.
3GPP, “Small cell enhancements for E-UTRA and E-UTRAN—Physical layer aspects,” 3rd Generation Partnership Project (3GPP), Technical Report (TR) 36.872, 12 2013, version 12.1.0. http://www.3gpp.org/-DynaReport/-36872.htm.
Mingoti, S. A., & Lima, J. O. (2006). Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms. European Journal of Operational Research, 174(3), 1742–1759.
Sofi, I. B., & Gupta, A. (2018). A survey on energy efficient 5G green network with a planned multi-tier architecture. Journal of Network and Computer Applications, 118, 1–28.
Bhushan, N., Li, J., Malladi, D., Gilmore, R., Brenner, D., Damnjanovic, A., et al. (2014). Network densification: The dominant theme for wireless evolution into 5G. IEEE Communications Magazine, 52(2), 82–89.
Zhang, H., Chu, X., Guo, W., & Wang, S. (2015). Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum. IEEE Communications Magazine, 53(3), 158–164.
Maksymyuk, T., Kyryk, M., & Jo, M. (2016). Comprehensive spectrum management for heterogeneous networks in LTE-U. IEEE Wireless Communications, 23(6), 8–15.
Araújo, D. C., Maksymyuk, T., de Almeida, A. L., Maciel, T., Mota, J. C., & Jo, M. (2016). Massive MIMO: Survey and future research topics. IET Communications, 10(15), 1938–1946.
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.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
About this article
Cite this article
Maksymyuk, T., Šlapak, E., Bugár, G. et al. Intelligent framework for radio access network design. Wireless Netw 26, 759–774 (2020). https://doi.org/10.1007/s11276-019-02172-7
- RAN workflow
- Artificial intelligence
- Self-supervised learning
- Big data