A bibliometric Analysis on Spectrum Sensing in Wireless Networks

TL;DR

This paper conducts a bibliometric analysis of spectrum sensing in wireless networks, highlighting the superiority of machine learning models over traditional methods at low SNR and recommending further research in this area.

Contribution

It provides a comprehensive bibliometric review of spectrum sensing techniques and identifies the need for improved models at low SNR conditions.

Findings

Machine learning models outperform traditional techniques at low SNR.

Hybrid models show promising results in spectrum sensing.

Further research is needed to develop models for low SNR scenarios.

Abstract

Spectrum scarcity is a prevalent problem in wireless networks due to the strict allotment of the spectrum (frequency bands) to licensed users by network regulatory bodies. Such an operation implies that the unlicensed users (secondary wireless spectrum users) have to evacuate the spectrum when the primary wireless spectrum users (licensed users) are utilizing the frequency bands to avoid interference. Cognitive radio alleviates the spectrum shortage by detecting unoccupied frequency bands. This reduces the underutilization of frequency bands in wireless networks. There have been numerous related studies on spectrum sensing, however, few studies have conducted a bibliometric analysis on this subject. The goal of this study was to conduct a bibliometric analysis on the optimization of spectrum sensing. The PRISMA methodology was the basis for the bibliometric analysis to identify the limitations of the existing spectrum sensing techniques. The findings revealed that various machine learning or hybrid models outperformed the traditional techniques such as matched filter and energy detectors at the lowest signal to noise ratio (SNR). SNR is the ratio of the desired signal magnitude to the background noise magnitude. This study, therefore, recommends researchers propose alternative techniques to optimize (improve) spectrum sensing in wireless networks. More work should be done to develop models that optimize spectrum sensing at low SNR.