Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.

Article

Download file Download BibTeX

Title

Computationally Efficient Wideband Spectrum Sensing through Cumulative Distribution Function and Machine Learning

Authors

[ 1 ] Instytut Telekomunikacji Multimedialnej, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2024

Published in

Journal of Communications Software and Systems

Journal year: 2024 | Journal volume: vol. 20 | Journal number: iss. 1

Article type

scientific article

Publication language

english

Keywords
EN
  • blind detection
  • cumulative distribution function
  • machine learning
  • spectrum sensing
  • unknown signals
Abstract

EN Blind spectrum sensing (BSS) is crucial for identifying unknown signals in scenarios with limited prior knowledge. Traditional methods face challenges with unknown and time-varying signals, especially in the presence of noise interference. This paper addresses these issues by introducing a statistical signal processing framework that extends the use of machine learning (ML) features. Our approach improves BSS by incorporating cumulative distribution functions (CDFs) into unsupervised ML, enabling effective clustering of diverse transmission states without assumptions about specific noise distributions. Additionally, we introduce a temporal decomposition technique using shorter Fast Fourier Transforms (FFTs), enhancing the learning process, reducing system inertia, and minimizing data requirements for retraining under dynamic conditions. We evaluate our method, focusing on various features/approaches for incorporating CDFs into ML, including centroid, linear approximation, and low-order statistics. Simulation results demonstrate robust detection in a standard transmission scenario with a Gaussian pulse amidst additive white Gaussian noise, maintaining a consistently low false alarm rate. These findings highlight our BSS approach’s effectiveness and practical potential in handling unknown signals in challenging environments. This research provides valuable insights, laying the groundwork for practical implementation in real-world scenarios.

Pages (from - to)

38 - 46

DOI

10.24138/jcomss-2023-0175

URL

https://stream.fesb.hr/10.24138/jcomss-2023-0175/

License type

CC BY-NC (attribution - noncommercial)

Full text of article

Download file

Access level to full text

public

Ministry points / journal

20

This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.