In this work, we consider a performance comparison of deep learning-based approaches to
the problem of spectrum sensing (SS) in cognitive radios. Towards this end, we use signal …

We consider supervised machine learning-based detection algorithms for spectrum sensing
in cognitive radio (CR) networks. The network comprises of multiple CR nodes, which collect …

Spectrum sensing plays an important role in enabling cognitive radio technology for the up-
and-coming generation of wireless communication systems. Over the last decade, several …

Different neural network (NN) architectures have different advantages. Convolutional neural
networks (CNNs) achieved enormous success in computer vision, while recurrent neural …

Spectrum sensing is a critical component of cognitive radio systems, enabling the detection
and utilization of underutilized frequency bands. Convolutional neural networks (CNNs) and …

Machine learning is the powerful tool of the artificial intelligence which is popularly
implemented in several applications. Spectrum sensing is the important function of a …

The Software-defined radio (SDR) technology is considered as a promising solution to
address the issue of spectrum scarcity by providing a high level of flexibility and …

In this work, we consider a supervised machine learning-based approach for spectrum
sensing in cognitive radios. The noise process is assumed to follow a generalized Gaussian …

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 …

Wireless devices are ubiquitous and consequently the spectrum is congested. Dynamic
spectrum access is becoming more widespread in unlicensed bands and as a means to …