Autors: Bandopadhaya, S., Samal, S.R., Poulkov, V. K.
Title: Machine learning enabled performance prediction model for massive-MIMO HetNet system
Keywords: 5G; Area spectral density; B5G wireless networks; Coverage p

Abstract: To support upcoming novel applications, fifth generation (5G) and beyond 5G (B5G) wireless networks are being propelled to deploy an ultra-dense network with an ultra-high spectral efficiency using the combination of heterogeneous network (HetNet) solutions and massive Multiple Input Multiple Output (MIMO). As the deployment of massive MIMO HetNet systems involves a high capital expenditure, network service providers need a precise performance analysis before investment. The performance of such networks is limited because of presence of inter-cell and intertier interferences. The conventional analytic approach to model the performance of such networks is not trivial, as the performance is a stochastic function of many network parameters. This paper proposes a machine learning (ML) approach to predict the network performance of a massive MIMO HetNet system considering a multi-cell scenario. This paper considers a two-tier network in which the base stations of each tier are equipped ..



    Sensors, vol. 21, issue 3, pp. 1-12, 2021, Switzerland, Multidisciplinary Digital Publishing Institute (MDPI), DOI 10.3390/s21030800

    Copyright MDPI

    Цитирания (Citation/s):
    1. Konan, N.F.R.O., Mwangi, E., Maina, C., "Enhancement of Signal to Interference plus Noise Ratio Prediction (SINR) in 5G Networks using a Machine Learning Approach", International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 319-328, 2022, DOI: 10.14445/22315381/IJETT-V70I10P231. - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science