Performance of Machine Learning-Based Techniques for Spectrum Sensing in Mobile Cognitive Radio Networks

Communication technologies are evolving drastically in recent years. However, the scarcity of spectrum began to appear with the accelerating usage of various communication technologies, as well as the preservation of traditional channel access methods. Cognitive Radio (CR) is an innovative solution for spectrum scarcity. Spectrum sensing is a key task of the CR life-cycle that gains significance as spectrum holes can be detected during this task. This paper studies and compares the performance of the
KMeans-based spectrum sensing technique
with the
non-cooperative spectrum sensing technique
, the
And-based spectrum sensing technique
, and the
Or-based spectrum sensing technique in stationary and mobile CR networks (CRNs).
The effect of the fading channel type has also been considered. Small-scale CRNs were simulated using the third version of the network simulator. In this context, two models have been developed. The first was built based on the well-known
$\kappa -\mu $
general fading model to simulate the fading effects. The latter is the noise model to simulate different noise conditions. The results reveal that spectrum sensing techniques provide better performance in stationary networks as compared to mobile networks. Further, our experimental results show that at least three secondary users and about 1500 samples are needed to reach acceptable performance. In addition, the results show that the
KMeans-based technique
slightly outperforms the
Or-based technique
, especially in highly noisy environments and under severe fading channels.