Abstract
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.
Highlights
IntroductionThe advent of new applications and technologies such as the Internet of Things, CyberPhysical Systems, etc., has propelled the demand for wireless spectrum [1]
We need to achieve a low probability of false alarm, between 0 to 0.1 according to the IEEE 802.22 standard [27,28], low sensing error, and high probability of detection
Spectrum detection is a big issue in cognitive radio
Summary
The advent of new applications and technologies such as the Internet of Things, CyberPhysical Systems, etc., has propelled the demand for wireless spectrum [1]. This increase in demand for spectrum cannot be achieved as a spectrum is a limited resource, and its expansion is difficult due to technological limitations. A spectrum is a precious resource, its utilization is sub-optimal as per the research finding of the Federal
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