Deep Learning for Spectrum Sensing in Cognitive Radio

• Main Authors: Surendra Solanki, Vasudev Dehalwar, Jaytrilok Choudhary
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Symmetry
• Subjects: cognitive radio; deep learning; spectrum sensing; convolutional neural network; long short term memory
• Online Access: https://www.mdpi.com/2073-8994/13/1/147

The detection of primary user signals is essential for optimum utilization of a spectrum by secondary users in cognitive radio (CR). The conventional spectrum sensing schemes have the problem of missed detection/false alarm, which hampers the proper utilization of spectrum. Spectrum sensing through deep learning minimizes the margin of error in the detection of the free spectrum. This research provides an insight into using a deep neural network for spectrum sensing. A deep learning based model, “DLSenseNet”, is proposed, which exploits structural information of received modulated signals for spectrum sensing. The experiments were performed using RadioML2016.10b dataset and the outcome was studied. It was found that “DLSenseNet” provides better spectrum detection than other sensing models.