  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.
