Spectrum Sensing in Non-Gaussian Noise

Deepa N Reddy; Yerram Ravinder

doi:10.17485/IJST/v14i32.1034

Article

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i32.1034

Year: 2021, Volume: 14, Issue: 32, Pages: 2596-2606

Original Article

Spectrum Sensing in Non-Gaussian Noise

Deepa N Reddy^1*, Yerram Ravinder²

¹Assistant Professor, Department of Electronics and Communication, BMS Institute of Technology & Management, Bengaluru, India
²Professor, Department of Electronics and Telecommunication, Pune Institute of Computer Technology, Pune, India

*Corresponding Author
Email: [email protected]

Received Date:06 June 2021, Accepted Date:29 August 2021, Published Date:27 September 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background: Spectrum sensing is a crucial step to realize the Cognitive Radio technology. The spectrum sensing schemes at low signal-to-noise ratio, noise uncertainty and especially under the background of non-Gaussian noise, provide low detection of the primary user. This results in missed detection or false alarm and increases higher interference to the primary user. Objectives: Detection schemes designed for additive Gaussian noise exhibit poor performance in the non-Gaussian environment. This study considers the problem of spectrum sensing with the assumption that the noise follows a non-Gaussian distribution with heavier tails. Methods/findings: A fuzzy logicbased method is proposed for primary user detection under non Gaussian Noise. The results are highlighted for the Laplacian noise. Through Monte Carlo simulations it is observed that Laplacian noise noticeably affects the performance of energy detector. Also, a fractional change in noise uncertainty degrades the performance of energy detector. The performance of the proposed scheme is presented through receiver operating characteristic (ROC) and plot of the detection probability versus signal-to-noise ratio (SNR) using simulations. It is shown that by appropriately choosing the membership functions and the fuzzy rule base in the fuzzy inference system the proposed fuzzy logic method for spectrum sensing provides reliable detection.

Keywords: NonGaussian noise; Fuzzy logic; Spectrum sensing; noise uncertainty

References

Kumar A, Thakur P, Pandit S, Singh G. Threshold selection and cooperation in fading environment of cognitive radio network: Consequences on spectrum sensing and throughput. AEU - International Journal of Electronics and Communications. 2020;117:153101. Available from: https://dx.doi.org/10.1016/j.aeue.2020.153101
Sarala B, Devi SR, Sheela JJJ. Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method. Computer Communications. 2020;152:1–7. Available from: https://dx.doi.org/10.1016/j.comcom.2019.12.058
Yu S, Liu J, Wang J, Ullah I. Adaptive Double-Threshold Cooperative Spectrum Sensing Algorithm Based on History Energy Detection. Wireless Communications and Mobile Computing. 2020;2020:1–12. Available from: https://dx.doi.org/10.1155/2020/4794136
Mahendru G, Shukla AK, Banerjee P, Patnaik LM. Adaptive Double Threshold Based Spectrum Sensing to Overcome Sensing Failure in Presence of Noise Uncertainty. 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). 2019;p. 466–471. Available from: https://doi.org/10.1109/SPIN.2019.8711570
Salahdine F, Kaabouch N, Ghazi HE. Techniques for dealing with uncertainty in cognitive radio networks. 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC). 2017;p. 1–6. Available from: https://doi.org/10.1109/CCWC.2017.7868352
Yang T, Wu Y, Li L, Xu W, Tan W. A Two-Step Cooperative Energy Detection Algorithm Robust to Noise Uncertainty. Wireless Communications and Mobile Computing. 2019;2019:1–10. Available from: https://dx.doi.org/10.1155/2019/3912784
Reddy DN, Ravinder Y. Novel receiver diversity combining methods for spectrum sensing using meta-analytic approach based on p-values. J. Eng. Sc. Tech. 2018;13(9):2883–2897.
Gao R, Qi P, Zhang Z. Frequency domain goodness of fit test based spectrum sensing method with dynamically varying noise. China Communications. 2020;17(12):172–179. Available from: https://dx.doi.org/10.23919/jcc.2020.12.012
Ahuja B, Kaur G. Two-Stage Spectrum Sensing Using Fuzzy Logic for Cognitive Radio Networks. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences. 2020;90(3):515–525. Available from: https://dx.doi.org/10.1007/s40010-019-00595-7
Bharatula S, Murthy BS. Innovative Fuzzy-Based Spectrum Sensing Technique for Noisy Conditions. In: Lecture Notes in Electrical Engineering. (pp. 735-750) Springer Singapore. 2021. 10.1007/978-981-15-9019-1_64
Mabrook MM, Taha HA, Hussein AI. Cooperative spectrum sensing optimization based adaptive neuro-fuzzy inference system (ANFIS) in cognitive radio networks. Journal of Ambient Intelligence and Humanized Computing. 2020;5:1–2. Available from: https://dx.doi.org/10.1007/s12652-020-02121-9
Sinha K, Trivedi YN. Spectrum Sensing Based on Dynamic Primary User with Additive Laplacian Noise in Cognitive Radio. In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. (pp. 16-28) Springer International Publishing. 2021.
Gao R, Jing F, Wang J, Zhang J, Zhang Y. A New Spectrum Sensing Method with Low SNR under Laplace Noise. 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI). 2020;p. 291–297. Available from: https://doi.org/10.1109/IICSPI51290.2020.9332346
Gao R, Qi P, Zhang Z. Performance analysis of spectrum sensing schemes based on energy detector in generalized Gaussian noise. Signal Processing. 2021;181:107893. Available from: https://dx.doi.org/10.1016/j.sigpro.2020.107893
Tan F, Song X, Leung C, Cheng J. Collaborative Spectrum Sensing in a Cognitive Radio System with Laplacian Noise. IEEE Communications Letters. 2012;16(10):1691–1694. Available from: https://dx.doi.org/10.1109/lcomm.2012.080312.120517
Ahuja B, Kaur G. Two-stage spectrum sensing using fuzzy logic for cognitive radio networks. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences. 2020;90:515–540.
Celikoglu A, Tirnakli U. Skewness and kurtosis analysis for non-Gaussian distributions. Physica A: Statistical Mechanics and its Applications. 2018;499:325–334. Available from: https://dx.doi.org/10.1016/j.physa.2018.02.035
Aryal GR. Study of laplace and related probability distributions and their applications. Available from: https://scholarcommons.usf.edu/etd/2443/
Gao R, Li Z, Li H, Ai B. Absolute Value Cumulating Based Spectrum Sensing with Laplacian Noise in Cognitive Radio Networks. Wireless Personal Communications. 2015;83(2):1387–1404. Available from: https://dx.doi.org/10.1007/s11277-015-2457-4
Tandra R, Sahai A. SNR Walls for Signal Detection. IEEE Journal of Selected Topics in Signal Processing. 2008;2(1):4–17. Available from: https://dx.doi.org/10.1109/jstsp.2007.914879

Copyright

© 2021 Reddy & Ravinder. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)