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Two-branch wavelet denoising for accurate spectrum sensing in cognitive radios

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Abstract

In order to detect the unused spectrum bands (the spectrum holes) efficiently in cognitive radios with low signal-to-noise radio (SNR), we propose to adopt two independent branches of wavelet to detect the singularities of the received signals’ power spectrum density (PSD). The sensing structure is flexible such that we can use one or two branches to cope with different SNRs. Under low SNR condition, each branch uses distinct characteristics between noise and signals in the wavelet transform to eliminate the singularities generated by the noise. By using bandpass filter to calculate PSD values of the subbands which are distinguished by the signal’s singularities, the subband with the minimum PSD value among all of the subbands could be found. Then, the results of the two branches are merged and analyzed in order to make the final decision. Finally, we use signal reconstruction to further remove the noise and then accurately detect the spectrum holes. When the SNR is high, only one branch through the denoising procedure is needed to get accurate sensing result. Our simulation results show that the two-branch wavelet method is more accurate than conventional approaches under given SNRs.

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References

  1. Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Computer Networks, 50(13), 2127–2159.

    Article  Google Scholar 

  2. Ariananda, D. D., Lakshmanan, M. K., & Nikooka, H. (2009). A study on the application of wavelet packet transforms to cognitive radio spectrum estimation. In Proceedings of the fourth international conference on CROWNCOM.

    Google Scholar 

  3. Cabric, D., Tkachenko, A., & Brodersen, R. W. (2006). Experimental study of spectrum sensing based on energy detection and network cooperation. In Proceedings of the first international workshop on technology and policy for accessing spectrum.

    Google Scholar 

  4. Haykin, S. (2005). Cognitive radio: brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

    Article  Google Scholar 

  5. Hur, Y., Park, J., & Woo, W. (2006). A cognitive radio (CR) system employing a dual-stage spectrum sensing technique: a multi-resolution spectrum sensing (MRSS) and a temporal signature detection (TSD) technique. In IEEE GLOBECOM.

    Google Scholar 

  6. Kim, S., Yoon, Y., Jeon, H., Kim, M., & Lee, H. (2008). Selective discrete wavelet packet transform-based energy detector for cognitive radios. In IEEE MILCOM.

    Google Scholar 

  7. Lee, W.-Y., & Akyildiz, I. F. (2008). Optimal spectrum sensing framework for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(10), 3845–3857.

    Article  Google Scholar 

  8. Mallat, S., & Hwang, W. L. (1992). Singularity detection and processing with wavelets. IEEE Transactions on Information Theory, 38(2), 617–643.

    Article  Google Scholar 

  9. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

    Article  Google Scholar 

  10. Sakran, H., & Shokair, M. (2011). Hard and softened combination for cooperative spectrum sensing over imperfect channels in cognitive radio networks. Telecommunication Systems. http://www.springerlink.com/content/h295572211018htv/.

  11. Tang, L., Chen, Q. B., Wang, G. Y., Zeng, X. P., & Wang, H. (2011). Opportunistic power allocation strategies and fair subcarrier allocation in OFDM-based cognitive radio networks. Telecommunication Systems. http://www.springerlink.com/content/g0g56801r0640m4g/.

  12. Tang, Z. H., Wei, G., & Zhu, Y. T. (2009). Weighted sum rate maximization for OFDM-based cognitive radio systems. Telecommunications Systems, 42, 77–84.

    Article  Google Scholar 

  13. Tian, Z., & Giannakis, G. B. (2006). A wavelet approach to wideband spectrum sensing for cognitive radios. In IEEE first international conference on cognitive radio oriented wireless networks and communications.

    Google Scholar 

  14. Wang, W., Wu, K. J., Luo, H. Y., Yu, G. D., & Zhang, Z. Y. (2011). Sensing error aware delay-optimal channel allocation scheme for cognitive radio networks. Telecommunication Systems. http://www.springerlink.com/content/t833p625265p0415/.

  15. Xu, Y.-L., Zhang, H.-S., & Han, Z.-H. (2009). The performance analysis of spectrum sensing algorithms based on wavelet edge detection. In Proceedings of the 5th international conference on wireless communications, networking and mobile computing.

    Google Scholar 

  16. Zhang, Y. H., & Leung, C. (2009). Cross-layer resource allocation for real-time services in OFDM-based cognitive radio systems. Telecommunications Systems, 42, 97–108.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by Program for Special grade of China Postdoctoral Science Foundation funded project (200902588), as well as National Natural Science Foundation of China (61072069).

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Authors and Affiliations

  1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, Shaanxi, P.R. China

    Xiaoyan Li, Fei Hu, Hailin Zhang & Chao Shi

  2. University of Alabama, Tuscaloosa, AL, USA

    Fei Hu

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  1. Xiaoyan Li
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  2. Fei Hu
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Correspondence to Xiaoyan Li.

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Li, X., Hu, F., Zhang, H. et al. Two-branch wavelet denoising for accurate spectrum sensing in cognitive radios. Telecommun Syst 57, 81–90 (2014). https://doi.org/10.1007/s11235-013-9787-x

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  • DOI: https://doi.org/10.1007/s11235-013-9787-x

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