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- W4313253984 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." @default.
- W4313253984 created "2023-01-06" @default.
- W4313253984 creator A5018323991 @default.
- W4313253984 creator A5063823021 @default.
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- W4313253984 date "2022-12-29" @default.
- W4313253984 modified "2023-09-25" @default.
- W4313253984 title "Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks" @default.
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- W4313253984 doi "https://doi.org/10.26636/jtit.2022.164922" @default.
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