Machine Learning-Based Small Cell Location Selection Process

• Autorzy: Wasilewska Małgorzata , Kułacz Łukasz

Identyfikatory

DOI

10.26636/jtit.2021.151021

• Języki publikacji: EN

Abstrakty

EN

In this paper, the authors present an algorithm for determining the location of wireless network small cells in a dense urban environment. This algorithm uses machine learning, such as k-means clustering and spectral clustering, as well as a very accurate propagation channel created using the ray tracing method. The authors compared two approaches to the small cell location selection process – one based on the assumption that end terminals may be arbitrarily assigned to stations, and the other assuming that the assignment is based on the received signal power. The mean bitrate values are derived for comparing different scenarios. The results show an improvement compared with the baseline results. This paper concludes that machine learning algorithms may be useful in terms of small cell location selection and also for allocating users to small cell base stations.

Słowa kluczowe

EN

base station selection; k-means clustering; spectral clustering; user equipment allocation

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2021
• Tom: nr 2
• Strony: 120--126
• Opis fizyczny: Bibliogr. 8 poz., rys.

Twórcy

autor

Wasilewska Małgorzata

• Institute of Radiocommunications, Faculty of Computing and Telecommunications, Poznań University of Technology, Polanka 3, 60-995 Poznań, Poland

• https://orcid.org/0000-0002-3471-0516

autor

Kułacz Łukasz

• Institute of Radiocommunications, Faculty of Computing and Telecommunications, Poznań University of Technology, Polanka 3, 60-995 Poznań, Poland

• https://orcid.org/0000-0002-3434-1917

Bibliografia

• [1] I. A. M. Balapuwaduge and F. Y. Li, „Hidden Markov model based machine learning for mMTC device cell association in 5G networks", in Proc. IEEE Int. Conf. on Commun. (ICC), Shanghai, China, 2019, pp. 1-6 (DOI: 10.1109/ICC.2019.8761913).
• [2] J. Yang, C. Wang, X. Wang, and C. Shen, „A machine learning approach to user association in enterprise small cell networks", in Proc. Int. Conference on Communications in China (ICCC), Beijing, China, 2018, pp. 850-854 (DOI: 10.1109/ICCChina.2018.8641148).
• [3] W. Qi, B. Zhang, B. Chen, and J. Zhang, „A user-based K-means clustering offloading algorithm for heterogeneous network", in Proc. 8th Annual Comput. and Commun. Workshop and Conf. (CCWC), Las Vegas, NV, 2018, pp. 307-312 (DOI: 10.1109/CCWC.2018.8301769).
• [4] Y. Xu, W. Xu, Z. Wang, J. Lin, and S. Cui, „Load balancing for ultradense networks: a deep reinforcement learning-based approach", IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9399-9412, 2019 (DOI: 10.1109/JIOT.2019.2935010).
• [5] R. de Paula Parisotto et al., „Drone base station positioning and power allocation using reinforcement learning", in Proc. 16th Int. Symp. on Wireless Commun. Systems (ISWCS), Oulu, Finland, 2019, pp. 213-217 (DOI: 10.1109/ISWCS.2019.8877247).
• [6] L. Wang, Y. Chao, S. Cheng, and Z. Han, „An integrated affinity propagation and machine learning approach for interference management in drone base stations", IEEE Trans. on Cognitive Commun. and Networking, vol. 6, no. 1, pp. 83-94, 2020 (DOI: 10.1109/TCCN.2019.2946864).
• [7] J. MacQueen, „Some methods for classiffication and analysis of multivariate observations", in Proc. of the fifth Berkeley Symp. on Mathematical Statistics and Probability, vol. 1, no. 14, 1967, pp. 281-297 [Online]. Available: http://digitalassets.lib.berkeley.edu/math/ucb/text/math s5 v1 article-17.pdf
• [8] U. Von Luxburg, „A tutorial on spectral clustering", Statistics and Computing, vol. 17, no. 4, pp. 395-416, 2007 (DOI: 10.1007/s11222-007-9033-z).