Traffic Learning: A Deep Learning Approach for Obtaining Accurate Statistical Information of the Channel Traffic in Spectrum Sharing Systems

Toma, Ogeen H ORCID: 0000-0002-3969-0470 and Lopez-Benitez, Miguel ORCID: 0000-0003-0526-6687 (2021) Traffic Learning: A Deep Learning Approach for Obtaining Accurate Statistical Information of the Channel Traffic in Spectrum Sharing Systems. IEEE ACCESS, 9. pp. 124324-124336.

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Official URL: http://dx.doi.org/10.1109/access.2021.3109861

Abstract

In recent works, the statistical information of the channel traffic has been increasingly exploited to make effective decisions in spectrum sharing systems. However, these statistics cannot be obtained perfectly under (realistic) Imperfect Spectrum Sensing (ISS). Therefore, in this work we study comprehensively the approaches in the literature that correct the estimation of the channel traffic statistics under ISS, namely the closed-form expression approach and the algorithmic reconstruction approach. Then, we introduce a novel approach named Traffic Learning as a Deep Learning (DL) approach for providing accurate estimation of the channel traffic statistics under ISS. For this novel approach, deep neural networks using Multilayer Perceptron (MLP) models are found for the estimation of several statistical metrics. In addition, we show that utilising effective features from spectrum sensing observations can lead to a considerable improvement in statistics estimation for each, mean, variance, minimum and distribution of the channel traffic under ISS, outperforming the existing approaches in the literature, which are based on either closed-form expressions or reconstruction algorithms.

Item Type:	Article
Uncontrolled Keywords:	Sensors, Estimation, Channel estimation, Deep learning, Closed-form solutions, Licenses, Artificial neural networks, Spectrum sharing, dynamic spectrum access, cognitive radio, channel traffic statistics, spectrum sensing, machine learning, deep learning
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	31 Aug 2021 15:27
Last Modified:	15 Mar 2024 06:08
DOI:	10.1109/ACCESS.2021.3109861
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3135418