Spectrum sensing techniques for 5G wireless networks: Mini review

Koteeshwari, R.S.; Malarkodi, B.

doi:10.1016/j.sintl.2022.100188

Cited by 9 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, it was noted that hybrid models outperform single models for spectrum sensing. This supports the findings by [Koteeshwari and Malarkodi, 2022; whose machine-learning or hybrid techniques were applied to optimize spectrum sensing as compared to traditional methods. This implies that for improving spectrum sensing at low SNR, machine learning / deep learning techniques should be adopted.…”

Section: Discussionsupporting

confidence: 86%

A Bibliometric Analysis on Spectrum Sensing in Wireless Networks

Tamuka¹,

Sibanda²

2023

INDJCSE

View full text Add to dashboard Cite

Spectrum scarcity is a prevalent problem in wireless networks due to network regulatory bodies' strict allotment of the spectrum (frequency bands) to licensed users. 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. This study's goal 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 spectrumsensing 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's 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.

show abstract

Section: Discussionsupporting

confidence: 86%