Improving the Performance of Heterogeneous Network Systems in Machine Learning-based 5G Mobile Communication System

  • Yoon-Hwan Kim
  • Dae-Young Lee
  • Sang-Hyun Bae
  • Tae Yeun Kim


Mobile traffic, which has increased significantly with the emergence of Fourth generation longterm evolution (4G-LTE) communications and advances in video streaming services, is still currently increasing at an incredible pace. Fifth-generation (5G) mobile communication systems, which were developed to deal with such a drastic increase in mobile traffic, aim to achieve ultra-high-speed data transmission, low latency, and the accommodation of many more connected devices compared to 4G-LTE systems. 5G communication uses high-frequency bandwidth to implement these features, which leads to an inevitable drawback of a high path loss. In order to overcome this disadvantage, small cell technology was developed, and is defined as small, low-power base stations that can extend the network coverage and solve the shadow area problem. Although small cell technology has these advantages, different problems, such as the effects of interference due to the deployment of a large number of small cells and the differences in devices accessing the network, need to be solved. To do so, it is necessary to develop an algorithm for a service method. However, general algorithms have difficulties in responding to the diverse environment of mobile communication systems, such as sudden increase in traffic in certain areas or sudden changes in the mobile population, and machine learning technology has been applied to solve this problem. This study employs a machine learning algorithm to determine small cell connections. In addition, a 5G macro system, the application of small cells, and the application of machine learning algorithms are compared to determine the performance improvement in the machine learning algorithm. Moreover, Support Vector Machine (SVM), Logistic Regression and Decision Tree algorithm are employed to show a training method that uses basic training data and a small cell on-off method, and the performance enhancement is verified based on this method.


[1] Jahng, J.H.; park, S.K. (2020). 5G Mobile Traffic Forecast, Electronics and Telecommunications Trends, 35(6), 129-136, 2020, DOI:10.22648/ETRI.2020.J.350613

[2] Bang, R.A.; Hong, S.E.; Song, J.T.; Kim, I.K.; Park, A.S.; Lee, M.S.; Jang, S.C. (2013). 5G Mobile Communication Technology trends, Information & communications magazine, 30(12), 25-36, 2013.

[3] Ericsson. (2019). Estimated growth of Mobile data Traffic and its distribution, Ericsson report, 2019.

[4] Kim, Y.H.; Bae, S.H.; Kim, T.Y. (2021). A Performance Improvement Technique Using Machine Learning in a Simulation-Based 5G-Small Cell Mobile Communication System, International Journal of Future Generation Communication and Networking, 14(3), 2021.

[5] Samsung. (2018). Who&How:Making 5G NR Standards, Samsung White Paper, 2018.

[6] Sulyman, A.I.; Nassar, A.T.; Samimi, M.K.; MacCartney, G.R.; Rappaport, T.S.; Alsanie, A. (2021). Radio propagation path loss models for 5G cellular networks in the 28 GHz and 38 GHz millimeter-wave bands, IEEE communications magazine, 52(9), 78-86, 2014, DOI: 10.1109/MCOM.2014.6894456

[7] Chung, W.G. (2018). Study on Effective 5G Network Deployment Method for 5G Mobile Communication Services, In The Journal of Korean Institute of Electromagnetic Engineering and Science, 29(5), 353-358, 2018. DOI:

[8] Patwary, M.; Sharma, S.K.; Chatzinotas, S.; Chen, Y.; Abdel-Maguid, M.; Abd-Alhameed, R.; Noras, J.; Ottersten, B. (2016). Universal Intelligent Small Cell (UnISCell) for next generation cellular networks, Digital Communications and Networks, 2(4), 167-174, 2016,

[9] [Online]. Available: https://, Informa Telecoms& Media, SmallCell Market Status, 2013.

[10] 5G.Forum (2018). Small Cell Ecosystem White Paper, 39-40, 2018.

[11] [Online]. Available:, Accesed on 8 Aptil 2013.

[12] Kim, K.; Myung, J.; Seo, J. (2018). Research Trends on Wireless Transmission and Access Technologies Using Deep Learning, Electronics and Telecommunications Trends, 33(5), 13-23, 2018, DOI:10.22648/ETRI.2018.J.330502

[13] Woo, Y.C.; Lee, S.Y.; Choi, W.; Ahn, C.W.; Baek, O.K. (2019). Trend of Utilization of Machine Learning Technology for Digital Healthcare Data Analysis, Electronics and Telecommunications Trends, 34(1), 98-10, 2019, DOI:10.22648/ETRI.2019.J.340109

[14] Zhang, A.; Lipton, Z.C.; Li, M.; Smola, A.J. (2021). Dive into deep learning, arXiv preprint arXiv:2106.11342.

[15] [Online]. Available: http://
How to Cite
KIM, Yoon-Hwan et al. Improving the Performance of Heterogeneous Network Systems in Machine Learning-based 5G Mobile Communication System. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 16, n. 6, nov. 2021. ISSN 1841-9844. Available at: <>. Date accessed: 22 jan. 2022. doi: