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Energy Detector with Adaptive Optimal Threshold for Enhancing Spectrum Sensing in Cognitive Radio Network

Authors

M.V.S. Sairam

Professor, Department of Electronics and Communication Engineering, Gayatri Vidya Parishad College of Engineering (A), Visakhapatnam, PIN: 530 048 (IN)

Raju Egala

Assistant Professor Department of Electronics and Communication Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A), Rushikonda, Visakhapatnam, 530 045 (IN)

Article Information

DOI: 10.51583/IJLTEMAS.2024.131218

Subject Category: Wireless Communication

Volume/Issue: 13/12 | Page No: 214-221

Publication Timeline

Submitted: 2025-01-10

Published: 2025-01-10

Abstract

Abstract: Cognitive radio (CR) is a promising solution to resolve the crisis of spectrum underutilization. Spectrum sensing is an indispensable aspect of CR network (CRN). Energy detection method is being recognized as a simple and reliable step for spectrum sensing. The significant factor of the energy detector (ED) is threshold, whose optimum value depends on signal to noise ratio (SNR). However, in a wireless environment, where the received signal is severely degraded due to the uncertain noise, reliable spectrum sensing is not guaranteed.


The key metrics of the CRN are total spectrum sensing error probability, throughput, and energy efficiency. For each SNR value, there exists an optimal threshold that minimizes total spectrum sensing error probability and maximizes throughput as well as energy efficiency. Therefore, the threshold of ED should be adaptive in CRN. This paper presents an optimal adaptive threshold by utilizing spectrum sensing errors for each metric in CRN.

Keywords

Cognitive radio, SNR, Optimal threshold, primary user, secondary user, spectrum sensing, throughput, energy efficiency

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References

1. Federal Communications Commission (2020). Spectrum policy task force report, FCC 02-155. [Google Scholar] [Crossref]

2. Yu, S., Liu, J., Wang, J., & Ullah, I. (2020). Adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. Wireless Communications and Mobile Computing, 2020, 1–12. [Google Scholar] [Crossref]

3. Sairam, M. V. S., Egala, R., & Nohith, K. S. (2024). Deep Learning Framework for Enhancing the Performance of Cognitive Radio Network. Journal of Emerging Technologies and Innovative Research (JETIR), 11(11). [Google Scholar] [Crossref]

4. Sairam, M. V. S., Egala, R., Rajasekhar, H., & Nohith, K. S. (2024). Deep Learning-Based Spectrum Management to Enhance the Performance of Cognitive Radio Network Using MobileNet. IRE Journals, 8(6), 274-279. [Google Scholar] [Crossref]

5. Sairam, M. V. S., & Sivaparvathi, M. (2017). Reduction of Reporting Time for Throughput Enhancement in Cooperative Spectrum Sensing Based Cognitive Radio. Network, 164. [Google Scholar] [Crossref]

6. Urriza, P., Rebeiz, E., & Cabric, D. (2013). Multiple antenna cyclostationary spectrum sensing based on the cyclic correlation significance test. IEEE Journal on Selected Areas in Communications, 31(11), 2185–2195. [Google Scholar] [Crossref]

7. Atapattu, S., Tellambura, C., Hai, J., & Rajatheva, N. (2014). Unified analysis of low-SNR energy detection and threshold selection. IEEE Transactions on Vehicular Technology, 64(11), 5006–5019. [Google Scholar] [Crossref]

8. Kozal, A.S.B., Merabti, M., & Bouhafs, F. (2012). An improved energy detection scheme for cognitive radio networks in low SNR region. In 17th IEEE Symposium on Computers and Communications, Cappadocia, Turkey, pp. 684–689. [Google Scholar] [Crossref]

9. Vladeanu, C., Nastase, V., & Martian, A. (2016). Energy detection algorithm for spectrum sensing using three consecutive sensing events. IEEE Wireless Communications Letters, 5(3), 284–287. [Google Scholar] [Crossref]

10. Urkowitz, H. (1967). Energy detection of unknown deterministic signals. IEEE Proceedings, 55(4), 523–531. [Google Scholar] [Crossref]

11. Yin, W., Ren, P., Su, Z., et al. (2011). A multiple antenna spectrum sensing scheme based on space and time diversity in cognitive radios. IEICE Transactions on Communications, 94(5), 1254–1264. [Google Scholar] [Crossref]

12. Tandra, R., & Sahai, A. (2005). Fundamental limits on detection in low SNR under noise uncertainty. In IEEE International Conference on Wireless Networks, Communications and Mobile Computing, Maui, HI, pp. 464–469. [Google Scholar] [Crossref]

13. Key, S.M. (1998). Fundamentals of Statistical Signal Processing, Detection Theory (Vol. 2). Prentice Hall. [Google Scholar] [Crossref]

14. Digham, F. F., Alouini, M. S., & Simon, M. K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55(1), 21–24. [Google Scholar] [Crossref]

15. Sairam, M. V. S., Riyaz, S., Madhu, R., & Harini, V. (2018). Low-complexity Selected Mapping Scheme Using a Bank of Butterfly Circuits in Orthogonal Frequency Division Multiplexing Systems. Wireless Personal Communications, 99(3), 1315-1328. [Google Scholar] [Crossref]

16. Riyaz, S., & Sairam, M. V. S. (2017). PAPR Reduction Using Extended BCH Code with Biasing Vector Technique in OFDM System. [Google Scholar] [Crossref]

17. Rajasekhar, H., Sairam, M. V. S., & Egala, R. (2024). SLM-Based PAPR Reduction in OFDM System Using Four Distinct Matrices. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), 11, 276-282. [Google Scholar] [Crossref]

18. Lavanya, K., & Sairam, M. V. S. (2015). Improvement of BER Performance in OFDM Under Various Channels with EH Code. International Journal of Advanced Research in Computer and Communication Engineering, 4(7), 131-134. [Google Scholar] [Crossref]

19. Sairam, M. V. S. (2017). PAPR Reduction in SLM Scheme Using Exhaustive Search Method. European Journal of Advances in Engineering and Technology, 4(10), 739-743. [Google Scholar] [Crossref]

20. Lavanya, K., & Sairam, M. V. S. (2017). Enhancement of Error Performance in OFDM System with Extended Hamming Code Under Various Channels. Journal of Network Communications and Emerging Technologies (JNCET), 7(10). [Google Scholar] [Crossref]

21. Sairam, M. V. S., & Prabhakara Rao, D. R. B. (2008). A Novel Coding Technique to Minimise the Transmission Bandwidth and Bit Error Rate in DPSK. IJCSNS, 8(5), 345. [Google Scholar] [Crossref]

22. Deepa, R., Karthick, R., Velusamy, J., & Senthilkumar, R. (2025). Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm. Computer Standards & Interfaces, 92, 103934. [Google Scholar] [Crossref]

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