Using Level Crossing Rate of Selection Combining Receiver Damaged by Beaulieu-Xie Fading and Rician Co-Channel Interference with a Purpose of Machine Learning QoS Level Prediction

Suad Suljovic; Dragana S. Krstic; Goran  Nestorovic; Nenad N. Petrovic; Sinisa  Minic; Devendra S. Gurjar

doi:10.5755/j02.eie.34018

Using Level Crossing Rate of Selection Combining Receiver Damaged by Beaulieu-Xie Fading and Rician Co-Channel Interference with a Purpose of Machine Learning QoS Level Prediction

Authors

Suad Suljovic The Academy of Applied Technical Studies Belgrade, Belgrade, Serbia https://orcid.org/0000-0003-1822-3782
Dragana S. Krstic Faculty of Electronic Engineering, University of Nis, Nis, Serbia
Goran Nestorovic The Academy of Applied Technical Studies Belgrade, Belgrade, Serbia https://orcid.org/0000-0002-7158-7078
Nenad N. Petrovic Faculty of Electronic Engineering, University of Nis, Nis, Serbia https://orcid.org/0000-0003-2264-7369
Sinisa Minic Faculty of Teacher Education in Prizren, University of Pristina in Kosovska Mitrovica, Leposavic, Serbia https://orcid.org/0000-0001-5656-5594
Devendra S. Gurjar Department of Electronics and Communications Engineering, National Institute of Technology Silchar, Assam, India https://orcid.org/0000-0002-9674-5378

DOI:

https://doi.org/10.5755/j02.eie.34018

Keywords:

Beaulieu-Xie fading, Machine learning, Rician co-channel interference, Selection combining

Abstract

In this paper, a wireless system is considered impacted by Beaulieu-Xie (BX) fading and Rician co-channel interference (CCI). A selection diversity combining technique is used to combat multi-path fading and CCI effects. The expression for the level crossing rate (LCR) is formulated for the receiver with more input branches, and the corresponding curves that have dependence on the signal-to-interference ratio (SIR) are drawn. On the basis of the presented graphs, we quantify the amount of LCR affected by the presence of fading and interference. Additionally, we propose a classification-based predictive model which leverages the previously calculated LCR value as one of inputs among other variables (number of users, base station, the day of the week) with the aim of Quality of Service (QoS) level prediction.

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Published

2023-06-27

How to Cite

Suljovic, S., Krstic, D. S., Nestorovic, G. ., Petrovic, N. N., Minic, S. ., & Gurjar, D. S. (2023). Using Level Crossing Rate of Selection Combining Receiver Damaged by Beaulieu-Xie Fading and Rician Co-Channel Interference with a Purpose of Machine Learning QoS Level Prediction. Elektronika Ir Elektrotechnika, 29(3), 68-73. https://doi.org/10.5755/j02.eie.34018

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Issue

Vol. 29 No. 3 (2023)

Section

TELECOMMUNICATIONS ENGINEERING

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