Comparative Study of 5G Signal Attenuation Estimation Models
Subject Areas : Communication Systems & Devices
Md Anoarul Islam
1
,
Manabendra Maiti
2
,
Judhajit Sanyal
3
*
,
Quazi Md Alfred
4
1 - Department of Electronics and Communication Engineering, Techno International New Town
2 - Department of Electronics and Communication Engineering, Techno International New Town
3 - Department of Electronics and Communication Engineering, Techno International New Town
4 - Department of Electronics and Communication Engineering, Aliah University
Keywords: 5G, Estimation, Attenuation Models, Machine Learning, Dynamic Model, Autoregression,
Abstract :
Wireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent years has consequently focused on establishment of robust and accurate attenuation models to estimate channel noise and subsequent signal loss. The identified challenge therefore is to identify or develop accurate computationally inexpensive models implementable on available hardware for generation of estimates with low error and validate the solutions experimentally. The present work surveys some of the most relevant recent work in this domain, with added emphasis on rain attenuation models and machine learning based approaches, and offers a perspective on the establishment of a suitable dynamic signal attenuation model for high-speed wireless communication in outdoor as well as indoor environments, presenting the performance evaluation of an autoregression-based machine learning model. Multiple versions of the model are compared on the basis of root mean square error (RMSE) for different orders of regression polynomials to find the best-fit solution. The accuracy of the technique proposed in the paper is then compared in terms of RMSE to corresponding moderate and high complexity machine learning techniques implementing adaptive spline regression and artificial neural networks respectively. The proposed method is found to be quite accurate with low complexity, allowing the method to be practically applicable in multiple scenarios.
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