An online adaptive cooperation scheme for spectrum sensing based on a second-order statistical method

Serhan Yarkan*, Behçet Uǧur Töreyin, Khalid A. Qaraqe, A. Enis Çetin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple CRs/sensors, improve the performance and reliability of spectrum sensing. Existing cooperation techniques usually assume a static communication scenario between the unknown source and sensors along with a fixed propagation environment class. In this paper, an online adaptive cooperation scheme is proposed for spectrum sensing to maintain the level of sensing reliability and performance under changing channel and environmental conditions. Each cooperating sensor analyzes second-order statistics of the received signal, which undergoes both correlated fast and slow fading. Autocorrelation estimation data from sensors are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projection onto convex sets. Numerical results show that the performance of the proposed scheme is maintained for dynamically changing characteristics of the channel between an unknown source and sensors, even under different physical propagation environments. In addition, it is shown that the proposed cooperative scheme, which is based on second-order detectors, yields better results compared with the same fusion mechanism that is based on conventional energy detectors.

Original languageEnglish
Article number6099645
Pages (from-to)675-686
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Issue number2
Publication statusPublished - Feb 2012
Externally publishedYes


Manuscript received April 27, 2011; revised September 27, 2011; accepted November 11, 2011. Date of publication December 9, 2011; date of current version February 21, 2012. This work was supported by Qatar National Research Fund (QNRF) Grant through National Priority Research Program (NPRP) 08-101-2-025. QNRF is an initiative of the Qatar Foundation. The review of this paper was coordinated by Prof. O. B. Akan.

FundersFunder number
National Priority Research Program08-101-2-025
Qatar National Research Fund


    • Adaptive data fusion (ADF)
    • Fast fading
    • Mobility
    • Online learning
    • Projection onto convex sets (POCS)
    • Shadowing
    • Spectrum sensing


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