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Weighted Eigenvalues based Spectrum Sensing for Cognitive Radio Systems

Published: 22 August 2019 Publication History

Abstract

Spectrum sensing in low signal to noise ratio region is of great significance in cognitive radio networks. Eigenvalue based spectrum sensing methods are generally used for detecting correlated signals. The performance of these algorithms degrades if the transmitted signal has low correlation. In this paper, a new eigenvalue-based algorithm is proposed for detecting uncorrelated signals. Weights are assigned to the maximum and minimum eigenvalues of the covariance matrix of received signal. Thereafter, the analytical expression for the probability of false alarm is derived by using Random Matrix Theorem. Numerical results verify the effectiveness of the proposed scheme.

References

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Mitola, J. and Maguire, G. 1999. Cognitive radio: Making software radios more personal, IEEE Personal Communication, vol. 6, no. 4, pp. 13--18.
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Mitola, J. 2000. Cognitive radio: An integrated architecture for software defined radio, PhD. Diss., Royal Institute of Technology, Stockholm, Sweden.
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Haykin, S. 2005. Cognitive radio: Brain-empowered wireless communications, IEEE Journal on Selected Areas in Communication, vol. 23, no. 2, pp. 201--220.
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Federal Communications Commission. 2003. Notice of proposed rulemaking and order, Facilitating opportunities for flexible, efficient and reliable spectrum use employing cognitive radio technologies, FCC 03--322.
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Zeng, Y., Koh, C. L., and Liang, Y. C. 2008. Maximum Eigenvalue Detection: Theory and Application, Communications, ICC '08. IEEE International Conference on, Beijing, 2008, pp. 4160--4164.
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Zeng, Y., and Liang, Y. C. 2009. Eigenvalue-based spectrum sensing algorithms for cognitive radio, in IEEE Transactions on Communications, vol. 57, no. 6, pp. 1784--1793.
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Penna, F., Garello, R., and Spirito, M. A. 2009. Cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices, in IEEE Communications Letters, vol. 13, no. 7, pp. 507--509.
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Yang, X., Lei, K., Peng, S., and Cao, X. 2011. Blind Detection for Primary User Based on the Sample Covariance Matrix in Cognitive Radio, in IEEE Communications Letters, vol. 15, no. 1, pp. 40--42.
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Guimaraes, D. A., de Souza, R. A. A., and Barreto, A. N. 2013. Performance of Cooperative Eigenvalue Spectrum Sensing with a Realistic Receiver Model under Impulsive Noise, in Journal of Sensors Actuator Network, vol. 2, pp. 46--69.
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Sharma, S. K., Chatzinotas, S., and Ottersten, B. 2013. Eigenvalue-Based Sensing and SNR Estimation for Cognitive Radio in Presence of Noise Correlation, in IEEE Transactions on Vehicular Technology, vol. 62, no. 8, pp. 3671--3684.
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Sharma, S. K., Chatzinotas, S., and Ottersten, B.2014. Maximum Eigenvalue detection for spectrum sensing under correlated noise, Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference on, Florence, 2014, pp. 7268--7272.
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Verma P., and Singh B. 2018. Performance Analysis of Various Eigenvalue-Based Spectrum Sensing Algorithms for Different Types of Primary User Signals. Advances in Electronics, Communication and Computing. Lecture Notes in Electrical Engineering, vol 443. Springer, Singapore
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Tracy, C. A., and Widom, H. 2000. The distribution of the largest eigenvalue in the Gaussian ensembles, in Calogero-Moser-Sutherland Models, J. van Diejen and L. Vinet, eds., pp. 461--472. New York: Springer.

Cited By

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  • (2024)A symmetric low-rank subspace clustering method for cooperative spectrum sensing in complex environmentsPhysical Communication10.1016/j.phycom.2024.10231363:COnline publication date: 25-Jun-2024
  • (2023)A Spectrum Sensing Method Based on Fusion Features and Online Learning in Time-Varying EnvironmentsIEEE Systems Journal10.1109/JSYST.2023.326622517:3(4833-4842)Online publication date: Sep-2023
  • (2023)Weighted Adaptive Spectrum Sensing for Cognitive Vehicular Networks2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307492(1-7)Online publication date: 6-Jul-2023

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    cover image ACM Other conferences
    BDIOT '19: Proceedings of the 3rd International Conference on Big Data and Internet of Things
    August 2019
    139 pages
    ISBN:9781450372466
    DOI:10.1145/3361758
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • University of Pisa: University of Pisa
    • La Trobe University

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 22 August 2019

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    Author Tags

    1. Cognitive radio
    2. Random matrix theory
    3. Spectrum Sensing
    4. Weighted Eigenvalues

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    View all
    • (2024)A symmetric low-rank subspace clustering method for cooperative spectrum sensing in complex environmentsPhysical Communication10.1016/j.phycom.2024.10231363:COnline publication date: 25-Jun-2024
    • (2023)A Spectrum Sensing Method Based on Fusion Features and Online Learning in Time-Varying EnvironmentsIEEE Systems Journal10.1109/JSYST.2023.326622517:3(4833-4842)Online publication date: Sep-2023
    • (2023)Weighted Adaptive Spectrum Sensing for Cognitive Vehicular Networks2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10307492(1-7)Online publication date: 6-Jul-2023

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