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A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network
Article

Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models

1
Department of Electrical and Electronics Engineering, Istanbul Medipol University, Istanbul 34810, Turkey
2
Department of Research & Development, Turkcell, Istanbul 34880, Turkey
3
Department of Computer Engineering, Federal University of Ceará (UFC), Sobral 62010-560, Brazil
4
Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Aygül, M.A.; Nazzal, M.; Ekti, A.R.; Görçin, A.; da Costa, D.B.; Ateş, H.F.; Arslan, H. Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM. In Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 25–28 May 2020.
Sensors 2021, 21(1), 135; https://doi.org/10.3390/s21010135
Received: 11 November 2020 / Revised: 12 December 2020 / Accepted: 22 December 2020 / Published: 28 December 2020
(This article belongs to the Special Issue AI-Enabled Cognitive Radio Networks)
In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results. View Full-Text
Keywords: cognitive radio; deep learning; multidimensions; real-world spectrum measurement; spectrum occupancy prediction cognitive radio; deep learning; multidimensions; real-world spectrum measurement; spectrum occupancy prediction
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MDPI and ACS Style

Aygül, M.A.; Nazzal, M.; Sağlam, M.İ.; da Costa, D.B.; Ateş, H.F.; Arslan, H. Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models. Sensors 2021, 21, 135. https://doi.org/10.3390/s21010135

AMA Style

Aygül MA, Nazzal M, Sağlam Mİ, da Costa DB, Ateş HF, Arslan H. Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models. Sensors. 2021; 21(1):135. https://doi.org/10.3390/s21010135

Chicago/Turabian Style

Aygül, Mehmet A.; Nazzal, Mahmoud; Sağlam, Mehmet İ.; da Costa, Daniel B.; Ateş, Hasan F.; Arslan, Hüseyin. 2021. "Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models" Sensors 21, no. 1: 135. https://doi.org/10.3390/s21010135

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