Cyclostationary and energy detection spectrum sensing beyond 5G waveforms

Arun Kumar; J Venkatesh; Nishant Gaur; Mohammed H. Alsharif; Peerapong Uthansakul; Monthippa Uthansakul; Arun Kumar; J Venkatesh; Nishant Gaur; Mohammed H. Alsharif; Peerapong Uthansakul; Monthippa Uthansakul

doi:10.3934/era.2023172

Electronic Research Archive

2023, Volume 31, Issue 6: 3400-3416. doi: 10.3934/era.2023172

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Research article

Cyclostationary and energy detection spectrum sensing beyond 5G waveforms

1.
Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, India
2.
Department of CSE, Chennai Institute of Technology, Chennai, India
3.
Department of Physics, JECRC University, Jaipur, India
4.
Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of Korea
5.
School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand

Received: 31 January 2023 Revised: 21 March 2023 Accepted: 05 April 2023 Published: 17 April 2023

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.
- cyclostationary,
- fifth generation,
- 5G,
- 6G,
- cognitive radio,
- CR,
- spectrum sensing
Citation: Arun Kumar, J Venkatesh, Nishant Gaur, Mohammed H. Alsharif, Peerapong Uthansakul, Monthippa Uthansakul. Cyclostationary and energy detection spectrum sensing beyond 5G waveforms[J]. Electronic Research Archive, 2023, 31(6): 3400-3416. doi: 10.3934/era.2023172

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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.

References

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