Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 5 November 2020 / Approved: 6 November 2020 / Online: 6 November 2020 (10:33:22 CET)

How to cite: 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. Preprints 2020, 2020110238 (doi: 10.20944/preprints202011.0238.v1). 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. Preprints 2020, 2020110238 (doi: 10.20944/preprints202011.0238.v1).

Abstract

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.

Subject Areas

cognitive radio; deep learning; multidimensions; real-world spectrum measurement; spectrum occupancy prediction

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.