Cyclostationary and energy detection spectrum sensing beyond 5G waveforms

Abstract

The cyclostationary spectrum (CS) method is one of the best at what it does because it effectively detects idle spectrum with low signal-to-noise ratios (SNR). In order to distinguish the signal in a noisy environment, gather more data that aids in a better analysis of signals, and use spectral correlation for dependable framework modelling, CS achieves optimal performance characteristics. High intricacy is seen as one of the CS's shortcomings. In this article, we suggest a novel CS algorithm for 5G waveforms. By restricting the computation of cyclostationary characteristics and the signal autocorrelation, the complexity of CS is reduced. To evaluate the performance of 5G waveforms, the Energy Detection (ED) and CS spectrum sensing algorithms based on cognitive radio (CR) are presented. The results of the study show that the suggested CS algorithm did a good job of detection and gained 2 dB compared to the conventional standards.