Analyzing the Impact of Impulsive Noise on spectrum sensing Techniques for Cognitive Radio Networks

Authors

  • Dipak Patil Sandip Institute of Engineering and Management, Nashik Maharashtra, India
  • Manoj Bhalerao PVG’s College of Engineerig,Nashik, Maharashtra, India
  • Vishal Wankhede S H H J B Polytechnic, Chandwad Maharashtra, India
  • Vijay Birari KBGT College of Engineerig,Nashik, Maharashtra, India
  • Rana Mahajan SVIT,Nashik, Maharashtra, India
  • Vinod Khairnar METs IOE,Nashik

Keywords:

Automatc Gain Control, Cognitive radio Networks, Energy detection, GLRT, spectrum sensing

Abstract

The widespread adoption of cognitive radio networks (CRNs) has led to increased interest in “spectrum sensing” techniques to efficiently detect and utilize underutilized frequency bands. However, the presence of impulsive noise poses a significant challenge to accurate “spectrum sensing”, as it can disrupt signal measurements and lead to false detections. This research paper aims to analyze the impact of impulsive noise on “spectrum sensing” techniques for CRNs. Impulsive noise is a type of noise that occurs randomly and can significantly affect the performance of cognitive radio systems. The assessment involves comparing the performance of different “spectrum sensing” techniques such as Energy detection (ED), Maximum-minimum eigenvalue detection (MMED), and Generalized Likelihood Ratio Test (GLRT) in the presence of impulsive noise. The analysis is done  by considering different metrics and optimum “spectrum sensing” model is proposed. This proposed work involves the use of a direct conversion receiver architecture with automatic gain control (AGC) to minimize noise and DC offset. The simulation scenarios involve different threshold and signal-to-noise ratio (SNR) levels to evaluate the performance of different “spectrum sensing” techniques in the presence of impulsive noise. The comparative evaluation is done by analyzing the graphical results obtained from the simulations and from results it is evident that the GLRT detection method exhibits better sensing ability in an impulsive.

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References

Federal Communications Commission.: Spectrum policy task force report, ET Docket No. 02-135, (2002)

Mitola, J., Maguire, and G.Q.: Cognitive Radio: making software radios more personal. IEEE Personal Commu.Mag., Vol.6, Issue 4, pp. 13–18 (1999)

Akyildiz, I.F.:Cooperative “spectrum sensing” in cognitive radio networks: A survey: a survey. In: Elsevier physical communication, pp.40-62 (2011).

Nadler, B., Garello, R.: Performance of Eigenvalue-Based Signal Detectors with Known and Unknown Noise Level. In Proceedings of IEEE International Conference on Communications, ICC, Kyto, Japan, pp.1-5 (2011)

Kortun, A., Ratnarajah, T., Sellathurai, M., Zhong, C., Papadias, C.: On the performance of eigenvalue-based cooperative “spectrum sensing” for cognitive radio. IEEE Journal on Sel. Top. Signal Processing, 49–55 (2011)

Tandra, R., Sahai, A.: Fundamental limits on detection in low SNR under noise uncertainty: in Proc. Int. Conf. Wireless Networks., Communications.Mobile Computing., Maui, HI, pp. 464–469 ( 2005)

Osorio, M-R., de Haro-Ariet, Calvo-Ramon, L., Sanchez L.M.: Performance evaluation of W-CDMA in actual impulsive noise scenarios using adaptive antennas. IEEE Proc. Communications, 151, pp. 589–594 ( 2004)

Budsabathon, M., Hara, S.: Robustness of OFDM Signal Against Temporally Localized Impulsive Noise. In Proceedings of the IEEE VTS 54th Vehicular Technology Conference, VTC 2001, 7–11; Vol. 3, pp. 1672–1676 (2001)

Kang, H.G., Song, I., Yoon, S., Kim, Y.H.: A class of spectrum-sensing schemes for cognitive radio under impulsive noise circumstances: Structure and performance in nonfading and fading environments. IEEE Trans. Veh. Tech., pp. 4322–4339(2010)

Guimarães,D.A.,.de Souza, R.A., dos Anjos.: Implementation orientation model for centralized data fusion cooperative spectrum. IEEE Commun. letter 16,pp.184-187, (2012)

Guimarães,D.A.,.de Souza, R.A., Barreto, A.N.: Performance of Cooperative Eigenvalue”spectrum sensing” with a Realistic Receiver Model under Impulsive Noise. Journal of Sensor and Actuator Networks, pp.46-59(2012)

Lago, J., Fernández, Salter, J.: Modeling Impulsive Interference in DVB-T: Statistical Analysis, Test Waveforms, and Receiver Performance. BBC R&D white paper WHP 080 (2004)

Yates, R.D., GoodmanD.:Probability, and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers. 2nded. (2004)

Razavi, B.: Design considerations for direct-conversion receivers. IEEE Trans. Circuits Syst. II, vol. 44, no. 6, pp. 428–43 (1997)

Dipak P. Patil ,Vijay M. Wadhai, “Performance Evaluation of “spectrum sensing” in Cognitive Radio for Conventional Discrete-time Memoryless MIMO Fading Channel Model,” in proceedings of IEEE International Conference on Collaborative Computing: Networking, Applications and Work sharing, at Miami Florida USA, pp. 425-430, Oct. 2014 .978-1-63190-043-3 © 2014 ICST.

Dipak P. Patil and Vijay M. Wadhai, “Performance Evaluation in Cognitive Radio Network over Wireless Fading Channels from “spectrum sensing” Perspective,” in proceddings of IEEE International Conference on Green Computing, Communications and Conservation of Energy, IEEE ICGCE-2013, at Kavaraipettai, Chennai, pp 53-58, Dec. 2013.978-1-4673-6126-2/13/$31.00 © 2013 IEEE.

Ms. Elena Rosemaro. (2014). An Experimental Analysis Of Dependency On Automation And Management Skills. International Journal of New Practices in Management and Engineering, 3(01), 01 - 06. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/25

Vijayalakshmi, V., & Sharmila, K. (2023). Secure Data Transactions based on Hash Coded Starvation Blockchain Security using Padded Ring Signature-ECC for Network of Things. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 53–61. https://doi.org/10.17762/ijritcc.v11i1.5986

Aoudni, Y., Donald, C., Farouk, A., Sahay, K. B., Babu, D. V., Tripathi, V., & Dhabliya, D. (2022). loud security based attack detection using transductive learning integrated with hidden markov model. Pattern Recognition Letters, 157, 16-26. doi:10.1016/j.patrec.2022.02.012

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Published

16.08.2023

How to Cite

Patil, D. ., Bhalerao , M. ., Wankhede, V. ., Birari, V. ., Mahajan, R. ., & Khairnar, V. . (2023). Analyzing the Impact of Impulsive Noise on spectrum sensing Techniques for Cognitive Radio Networks. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 727–733. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3327

Issue

Vol. 11 No. 10s (2023)

Section

Research Article

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