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Edge-Fitting Based Energy Detection for Cognitive Radios

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Abstract

Two well-known drawbacks of energy detection sensing are its poor performance at low SNR and dependency on the accurate knowledge of noise power. To overcome these challenges in the context of wideband sensing, this paper proposes a spectrum sensing algorithm, inspired from edge detection techniques used in image processing. It is an improved, detailed and extended version of the gradient based method reported earlier (Koley et al. IEEE Communications Letters 19, 391–394, 2015). It is characterized by (i) histogram based noise power estimation for dynamic threshold computation (ii) preprocessing to obtain high detection probability with fewer numbers of samples and (iii) use of edge values as decision metric. Monte Carlo simulations as well as real-time wideband-sensed data captured through a Universal Software Radio Peripheral (USRP) is used to evaluate the performance of the algorithm. Mathematical formulations for the position of “SNR wall” and detection performance at different signal bandwidths is derived. Computational complexity of the edge-fitting scheme is shown to be lower than some of the wavelet approaches.

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References

  1. Koley, S., Mirza, V., Islam, S., & Mitra, D. (2015). Gradient based real-time spectrum sensing at low SNR. IEEE Communications Letters, 19, 391–394.

    Article  Google Scholar 

  2. Spectrum policy task force report. (2002). Fed. Commun. Commission, Washington, DC, Tech, ET Docket; 02–135.

  3. Haykin, S. (2005). Cognitive radio: brain-empowered wireless communications. IEEE Transactions on Communications, 23, 201–220.

    Google Scholar 

  4. Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2008). A survey on spectrum management in cognitive radio networks. IEEE Communications Magazine, 46, 40–48.

    Article  Google Scholar 

  5. Ling, X., Wu, B., Wen, H., Ho, P., Bao, Z., & Pan, L. (2012). Adaptive threshold control for energy detection based spectrum sensing in cognitive radios. IEEE Wireless Communications Letters, 1, 448–451.

    Article  Google Scholar 

  6. Joshi, D. R., Popescu, D. C., & Dobre, O. A. (2011). Gradient-based threshold adaptation for energy detector in cognitive radio systems. IEEE Communications Letters, 15, 19–21.

    Article  Google Scholar 

  7. Sutton, P. D., Nolan, K. E., & Doyle, L. E. (2008). Cyclostationary signatures in practical cognitive radio applications. IEEE Journal on Selected Areas in Communications, 26, 13–24.

    Article  Google Scholar 

  8. Zeng, Y., & Liang, Y. C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57, 1784–1793.

    Article  Google Scholar 

  9. Saarnisaari, H., Henttu, P., & Juntti, M. (2005). Iterative multidimensional impulse detectors for communications based on the classical diagnostic methods. IEEE Transactions on Communications, 53, 395–398.

    Article  Google Scholar 

  10. Vartiainen J, Lehtomaki J, Saarnisaari H. (2005). Double-threshold based narrowband signal extraction. IEEE Vehicular Tech. Conf.

  11. Lehtomäki, J., Vartiainen, J., Juntti, M., & Saarnisaari, H. (2008). Analysis of the LAD methods. IEEE Signal Processing Letters, 15, 237–240.

    Article  Google Scholar 

  12. Wang, N., Gao, Y., & Zhang, X. (2013). Adaptive spectrum sensing algorithm under different primary user utilizations. IEEE Communications Letters, 17, 1838–1841.

    Article  Google Scholar 

  13. Fuentes, L. G., Barbé, K., Moer, W., & Bjorsell, N. (2013). Cognitive radios: discriminant analysis for automatic signal detection in measured power spectra. IEEE Transactions on Instrumentation and Measurement, 62, 3351–3360.

    Article  Google Scholar 

  14. Datla, D., Wyglinski, A. M., & Minden, G. J. (2009). A Spectrum surveying framework for dynamic spectrum access networks. IEEE Transactions on Vehicular Technology, 58, 4158–4168.

    Article  Google Scholar 

  15. Tandra, R., & Sahai, A. (2008). SNR walls for signal detection. IEEE Journal on Selected Topics in Signal Processing, 2, 4–17.

    Article  Google Scholar 

  16. Sun, H., Chiu, W., Jiang, J., Nallanathan, A., & Poor, H. V. (2012). Wideband spectrum sensing with sub-syquest sampling in cognitive radios. IEEE Transactions on Signal Processing, 60, 6068–6073.

    Article  MathSciNet  Google Scholar 

  17. Yarkan, S. (2015). A generic measurement setup for implementation and performance evaluation of spectrum sensing techniques: Indoor environments. IEEE Transactions on Instrumentation and Measurement, 64, 606–614.

    Article  Google Scholar 

  18. Hamid, M., Bjorsell, N., Moer, W., Barbe, K., & Slimane, S. (2013). Blind spectrum sensing for cognitive radios using discriminant analysis: a novel approach. IEEE Transactions on Instrumentation and Measurement, 62, 2912–2921.

    Article  Google Scholar 

  19. Bao, D., Vito, L., & Rapuano, S. (2013). A histogram-based segmentation method for wideband spectrum sensing in cognitive radios. IEEE Transactions on Instrumentation and Measurement, 62, 1900–1908.

    Article  Google Scholar 

  20. Coudray, N., Buessler, J., & Urban, J. (2010). Robust threshold estimation for images with unimodal histograms. Pattern Recog. Lett., Elsevie, 31, 1010–1019.

    Article  Google Scholar 

  21. El-Khamy S, El-Mahallawy M, Youssef E. (2013) Improved wideband spectrum sensing techniques using wavelet-based edge detection for cognitive radio. IEEE Int. Conf. on Comp. Netw. and Commun. (ICNC) 418–423.

  22. Pratt, W. K. (2007). Digital image processing (4th ed.). Hoboken: john wiley & sons, inc.

    Book  Google Scholar 

  23. Lee, Y., & Tantaratana, S. (1991). A simple formula for the variance of the median filter output with laplacian input. IEEE Transactions on Signal Processing, 39, 984–986.

    Article  Google Scholar 

  24. Tian Z, Giannakis GB. (2006) A wavelet approach to wideband spectrum sensing for cognitive radios. Int. Conf. on Cognitive Radio Oriented Wireless Netw. Commun 1–5.

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Acknowledgements

This work was supported by the UGC Major Research Project of India under Grant 42-118/2013(SR).

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Authors and Affiliations

  1. Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Jharkhand, 826004, India

    Santasri Koley & Debjani Mitra

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  1. Santasri Koley
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  2. Debjani Mitra
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Correspondence to Santasri Koley.

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Koley, S., Mitra, D. Edge-Fitting Based Energy Detection for Cognitive Radios. J Sign Process Syst 90, 1687–1698 (2018). https://doi.org/10.1007/s11265-017-1320-0

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  • DOI: https://doi.org/10.1007/s11265-017-1320-0

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