Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks

Authors

  • Sanjeevkumar Jeevangi
  • Shivkumar Jawaligi
  • Vilaskumar Patil

DOI:

https://doi.org/10.26636/jtit.2022.164922

Keywords:

cognitive radio, improved NMF, LU-SLNO system, optimized CNN, spectrum sensing

Abstract

Vacant frequency bands are used in cognitive radio (CR) by incorporating the spectrum sensing (SS) technique. Spectrum sharing plays a central role in ensuring the effectiveness of CR applications. Therefore, a new multi-stage detector for robust signal and spectrum sensing applications is introduced here. Initially, the sampled signal is subjected to SNR estimation by using a convolutional neural network (CNN). Next, the detection strategy is selected in accordance with the predicted SNR levels of the received signal. Energy detector (ED) and singular value-based detector (SVD) are the solutions utilized in the event of high SNR, whilst refined non-negative matrix factorization (MNMF) is employed in the case of low SNR. CNN weights are chosen via the Levy updated sea lion optimization (LU-SLNO) algorithm inspired by the traditional sea lion optimization (SLNO) approach. Finally, the outcomes of the selected detectors are added, offering a precise decision on spectrum tenancy and existence of the signal.

Downloads

Download data is not yet available.

Downloads

Published

2022-12-30

How to Cite

Jeevangi, S., Jawaligi, S., & Patil, V. (2022). Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks. Journal of Telecommunications and Information Technology, (4), 21–32. https://doi.org/10.26636/jtit.2022.164922

Issue

Section

ARTICLES FROM THIS ISSUE

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.