- Tytuł:
- Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks
- Autorzy:
-
Jeevangi, Sanjeevkumar
Jawaligi, Shivkumar
Patil, Vilaskumar - Tematy:
-
cognitive radio
improved NMF
LU-SLNO system
optimized CNN
spectrum sensing - Pokaż więcej
- Wydawca:
- Instytut Łączności - Państwowy Instytut Badawczy
- Powiązania:
- https://bibliotekanauki.pl/articles/2174451.pdf Link otwiera się w nowym oknie
- Opis:
- 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.
- Dostawca treści:
- Biblioteka Nauki
Artykuł