Modified Threshold - based Intelligent Enhanced Energy Detector for Cognitive Radio Networks

Authors

  • Narendrakumar Chauhan Faculty of Technology, D. D. University, Gujarat 387001, INDIA
  • Sagar Kavaiya Faculty of Computer Science and Application, Charotar University, Changa, Gujarat, INDIA.
  • Purvang Dalal Faculty of Technology, D. D. University, Gujarat 387001, INDIA

Keywords:

Spectrum Sensing, Inter Branch Correlation, Nakagami-m fading channel, Imperfect Channel State Information

Abstract

This paper introduces an enhanced energy detector (IED) that utilizes the Nakagami-m fading channel with maximal ratio combining (MRC) in multiple-input multiple-output (MIMO) configurations. The main objective of the research is to optimize the performance of a cognitive radio (CR) system consisting of

Downloads

Download data is not yet available.

References

M. Vegni and D. P. Agrawal, Cognitive Vehicular Net- works. CRC Press, 2016.

H. Urkowitz, “Energy detection of unknown deterministic signals,” Proc. IEEE, vol. 55, no. 4, pp. 523–531, 1967.

T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Commun. Surveys & Tuts., vol. 11, no. 1, pp. 116–130, 2009.

Y. Chen, “Improved energy detector for random signals in Gaussian noise,” IEEE Trans. Wireless Commun., vol. 9, no. 2, pp. 558–563, 2010.

A. Singh, M. R. Bhatnagar, and R. K. Mallik, Cooperative spectrum sensing with an improved energy detector in cognitive radio network,” in Proc. of IEEE NCC, 2011, pp. 1–5.

A. Singh, M. R. Bhatnagar, and R. K. Mallik, “Cooperative

spectrum sensing in multiple antennas based cognitive radio network using an improved energy detector,” IEEE Com- mun. Lett., vol. 16, no. 1, pp. 64–67, January 2012.

L. Gahane, P. K. Sharma, N. Varshney, T. A. Tsiftsis, and

P. Kumar, “An improved energy detector for mobile cognitive users over generalized fading channels,” IEEE Trans. Com- mun., vol. 66, no. 2, pp. 534–545, 2017.

L. Gahane and P. K. Sharma, “Performance of improved en-

ergy detector with cognitive radio mobility and imperfect channel state information,” IET Commun., vol. 11, no. 12, pp. 1857–1863, 2017.

V. A. Aalo, “Performance of maximal-ratio diversity systems

in a correlated Nakagami-fading environment,” IEEE Trans. Commun., vol. 43, no. 8, pp. 2360–2369, 1995.

S. Kavaiya, D. K. Patel, Y. L. Guan, S. Sun, Y. C. Chang,

and J. M. Lim, “On the energy detection performance of ar- bitrarily correlated dual antenna receiver for vehicular com- munication,” IEEE Commun. Lett., vol. 23, no. 7, pp. 1186– 1189, July 2019.

H. S. Abed and H. N. Abdullah, “Improvement of spectrum

sensing performance in cognitive radio using modified hybrid sensing method,” Acta Polytechnica, vol. 62, no. 2, pp. 228– 237, 2022.

S. S. Chopade and S. S. Dalu, “Improving security with

optimized qos in cognitive radio networks using ai backed blockchains,” in ICCCE 2021. Springer, 2022, pp. 629–638.

P. Skokowski, K. Malon, and J. Łopatka, “Building the elec-

tromagnetic situation awareness in manet cognitive radio networks for urban areas,” Sensors, vol. 22, no. 3, p. 716, 2022.

N. Chaudhary and R. Mahajan, “Comprehensive review on

spectrum sensing techniques in cognitive radio,” Engineer- ing Review: 42(1), 88-102, http:/doi.org/10.30765/er.1677,

vol. 42, no. 1, pp. 0–0, 2022.

S. Madbushi and M. Rukmini, “Mitigation of primary user emulation attack using a new energy detection method in cognitive radio networks,” Journal of Central South Univer- sity, vol. 29, no. 5, pp. 1510–1520, 2022.

H. S. Abed and H. N. Abdullah, “Improvement of spectrum

sensing performance in cognitive radio using modified hybrid sensing method,” Acta Polytechnica, vol. 62, no. 2, pp. 228– 237, 2022.

S. Murugan Suganthi and G. Ramaswamy Shunmugavel, “Improved hard fusion methods for enhancing detection and energy efficiency in cognitive radio networks,” Concurrency and Computation: Practice and Experience, vol. 34, no. 5, p. e6686, 2022.

X. Zhang, N. Chen, N. Zhao, Y. Luo, W. Kan, and S. Yang,

“Spectrum allocation and power control in OFDM systems for cognitive radio networks,” in 2022 9th international Con- ference on Wireless Communication and Sensor Networks (ICWCSN), 2022, pp. 57–64.

M. Shili, M. Hajjaj, and M. L. Ammari, “User clustering and power allocation for massive mimo with noma-inspired cog- nitive radio,” IEEE Transactions on Vehicular Technology, 2022.

A. Kumar, P. Thakur, S. Pandit, and G. Singh, “Analysis of optimal threshold selection for spectrum sensing in a cogni- tive radio network: an energy detection approach,” Wireless Networks, vol. 25, no. 7, pp. 3917–3931, 2019.

B. Sarala, S. R. Devi, and J. J. J. Sheela, “Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method,” Computer Communications, vol. 152, pp. 1–7, 2020.

W. Wu, Z. Wang, L. Yuan, F. Zhou, F. Lang, B. Wang, and

Q. Wu, “Irs-enhanced energy detection for spectrum sensing in cognitive radio networks,” IEEE Wireless Communications Letters, vol. 10, no. 10, pp. 2254–2258, 2021.

S. Yu, J. Liu, J. Wang, and I. Ullah, “Adaptive double- threshold cooperative spectrum sensing algorithm based on history energy detection,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1–12, 2020.

Li, Xingwang, Yu, Shanshan, Wang, Jing, and Ullah, Inam “Adaptive double-threshold cooperative spectrum sens- ing algorithm based on history energy detection,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1–12, 2020.

S. Al-Juboori and X. N. Fernando, “Multiantenna spectrum sensing over correlated Nakagami-

Downloads

Published

12.07.2023

How to Cite

Chauhan, N. ., Kavaiya , S. ., & Dalal, P. . (2023). Modified Threshold - based Intelligent Enhanced Energy Detector for Cognitive Radio Networks. International Journal of Intelligent Systems and Applications in Engineering, 11(9s), 120–130. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3100

Issue

Section

Research Article