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

Sensors

DanielBenevides da Costa¹  MehmetAli Aygül²  Mahmoud Nazzal²  Hüseyin Arslan²  HasanFehmi Ateş²  Mehmetİzzet Sağlam³ 

[1] Department of Computer Engineering, Federal University of Ceará (UFC), Sobral 62010-560, Brazil;Department of Electrical and Electronics Engineering, Istanbul Medipol University, Istanbul 34810, Turkey;Department of Research & Development, Turkcell, Istanbul 34880, Turkey;

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

DOI  :  10.3390/s21010135

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown