Hybrid Spectrum Sensing Enhancement for Cognitive Radio in 6G Radio System
DOI:
https://doi.org/10.3103/S0735272723050023Keywords:
6G, Spectrum Sensing, Hybrid Algorithm, MF, Energy DetectionAbstract
This article highlights the potential advantages of the proposed hybrid algorithm for 6G. The algorithm's adaptability addresses the dynamic spectral characteristics of 6G environments, enabling seamless transition between different spectral conditions. The hybrid approach holds promise for enhancing spectrum utilization, reducing interference, and optimizing overall communication system performance. As 6G technology evolves, the integration of Energy Detection and Cyclostationary Spectrum Sensing through the hybrid algorithm offers a glimpse into innovative techniques that can shape the future of wireless communication, unlocking the potential for enhanced connectivity, efficiency, and user experience. Several parameters such as Probability of detection (Pd), Probability of false alarm (pfa), Biter error rate (BER) and Power spectral density (PSD) are compared and analysed with the conventional spectrum sensing algorithms. It is seen that the proposed hybrid algorithms obtained a substantial detection performance at the low Signal to noise ratio (SNR).
References
. Wu, Q., Wang, W., Li, Z. et al. SpectrumChain: a disruptive dynamic spectrum-sharing framework for 6G. Sci. China Inf. Sci. 66, 130302 (2023). https://doi.org/10.1007/s11432-022-3692-5
. A. Kumar, M. A. Albreem, M. Gupta, M. H. Alsharif and S. Kim, "Future 5G Network Based Smart Hospitals: Hybrid Detection Technique for Latency Improvement," in IEEE Access, vol. 8, pp. 153240-153249, 2020, doi: 10.1109/ACCESS.2020.3017625.
. A. Ivanov, K. Tonchev, V. Poulkov and A. Manolova, "Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey," in IEEE Access, vol. 9, pp. 116994-117026, 2021, doi: 10.1109/ACCESS.2021.3106235.
. Kumar, Arun, J Venkatesh, Nishant Gaur, Mohammed H. Alsharif, Abu Jahid, and Kannadasan Raju. 2023. "Analysis of Hybrid Spectrum Sensing for 5G and 6G Waveforms" Electronics 12, no. 1: 138. https://doi.org/10.3390/electronics12010138.
. P. Deepanramkumar and N. Jaisankar, "BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles," in IEEE Access, vol. 10, pp. 74220-74243, 2022, doi: 10.1109/ACCESS.2022.3187745.
. Z. Wei et al., "Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey," in IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11068-11092, 1 July1, 2023, doi: 10.1109/JIOT.2023.3235618.
. C. Chaccour, M. N. Soorki, W. Saad, M. Bennis, P. Popovski and M. Debbah, "Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing," in IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 967-993, Secondquarter 2022, doi: 10.1109/COMST.2022.3143454.
. kockaya, K., Develi, I. Spectrum sensing in cognitive radio networks: threshold optimization and analysis. J Wireless Com Network 2020, 255 (2020). https://doi.org/10.1186/s13638-020-01870-7.
. Arun Kumar, Mohit Kumar Sharma, Kanchan Sengar and Suraj Kumar, "NOMA based CR for QAM-64 and QAM-256", Egyptian Informatics Journal, 21 (2020) 67–71, 2021.
. 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.
. Arun Kumar and NandhaKumar P, "OFDM system with cyclostationary feature detection spectrum sensing",
ICT Express, 5 (2019), pp. 21–25, 2019.
. Martian A, Al Sammarraie MJA, Vlădeanu C, Popescu DC. Three-Event Energy Detection with Adaptive Threshold for Spectrum Sensing in Cognitive Radio Systems. Sensors. 2020; 20(13):3614. https://doi.org/10.3390/s20133614.
. Lorincz, Josip, Ivana Ramljak, and Dinko Begusic. 2021. "Algorithm for Evaluating Energy Detection Spectrum Sensing Performance of Cognitive Radio MIMO-OFDM Systems" Sensors 21, no. 20: 6881. https://doi.org/10.3390/s21206881.
. M Chaitra and Somnath Sinha, "Spectrum Sensing in Cognitive Radio using Energy Detection: Comprehensive Analysis",Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India, 2021.DOI: 10.4108/eai.16-5-2020.2303966.
.Manish Kumar and Saikat Majumder, "Cooperative Spectrum Sensing Using Extreme Learning Machines for Cognitive Radio Networks",IETE Technical Review, Volume 39, 2022 - Issue 3, pp. 698-712, 2021.
. Manish Kumar and Saikat Majumder, "Cooperative Spectrum Sensing Using Extreme Learning Machines for Cognitive Radio Networks",IETE Technical Review, Volume 39, 2022 - Issue 3, pp. 698-712, 2021.
. X. Fang, W. Feng, Y. Chen, N. Ge and Y. Zhang, "Joint Communication and Sensing Toward 6G: Models and Potential of Using MIMO," in IEEE Internet of Things Journal, vol. 10, no. 5, pp. 4093-4116, 1 March1, 2023, doi: 10.1109/JIOT.2022.3227215.
. M. Ashraf, B. Tan, D. Moltchanov, J. S. Thompson and M. Valkama, "Joint Optimization of Radar and Communications Performance in 6G Cellular Systems," in IEEE Transactions on Green Communications and Networking, vol. 7, no. 1, pp. 522-536, March 2023, doi: 10.1109/TGCN.2023.3234258.
