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Advanced Energy Sensing Techniques Implemented Through Source Number Detection for Spectrum Sensing in Cognitive Radio

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Computational Vision and Robotics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 332))

Abstract

The world of wireless technology is been one of the most progressive and challenging aspects for the users and providers. It deals with the wireless spectrum whose efficient use is of foremost concern. These are improved by the cognitive radio users for their noninterference communication with the licensed users. Spectrum holes detection and sensing is a dynamic time variant function which is been modified using the proposed source number detection and energy detection. Energy detection technique is implemented so as to compare the thresholds of the channels dynamically, and source detection method is used for predicting the number of channels where the energy detection is to be performed. The simulation results show the optimization and reduced probability of miss detection considering the change in threshold.

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References

  1. Yücek, T., Arslan, H.: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutorials 11(1), 116–130 (2009)

    Google Scholar 

  2. Broderson, R.W., Wolisz, A., Cabric, D., Mishra, S.M., Willkomm, D.: Corvus: a cognitive radio approach for usage of virtual unlicensed spectrum. White paper (2004)

    Google Scholar 

  3. Mitola III, J.: An integrated agent architecture for software defined radio. Dissertation (2000). ISSN 1403-5286 ISRN KTH/IT/AVH—00/01—SE

    Google Scholar 

  4. Chunli, D., Yuning, D., Li, W.: Autoregressive channel prediction model for cognitive radio. In: 5th International Conference on Wireless Communications, Networking and Mobile Computing, WiCom ’09, pp. 1–4. Beijing, China (2009)

    Google Scholar 

  5. Khajavi, N.T., Ivrigh, S.S., Sadough, S.M.S.: A novel framework for spectrum sensing in cognitive radio networks. IEICE Trans. Commun. E9-B(9), 2600–2609 (2011)

    Google Scholar 

  6. Ma, J., Li, Y.: Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. In: Proceedings of IEEE Global Telecommunication Conference, pp. 3139–3143 (2007)

    Google Scholar 

  7. Quan, Z., Cui, S., Poor, H.V., Sayed, A.H.: Collaborative wideband sensing for cognitive radios. IEEE Signal Process. Mag. 25(6), 63–70 (2008)

    Google Scholar 

  8. Arslan, H., Yarkan, S.: Binary time series approach to spectrum prediction for cognitive radio. In: IEEE 66th Vehicular Technology Conference, pp. 1563–1567. Baltimore, USA (2007)

    Google Scholar 

  9. Qiu, R.C., Chen, Z.: Prediction of channel state for cognitive radio using higher-order hidden Markov model. In: Proceedings of the IEEE SoutheastCon, pp. 276–282. Concord, USA (2010)

    Google Scholar 

  10. Hyvarinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)

    Article  Google Scholar 

  11. Li, Y., Dong, Y.N., Zhang, H., Zhao, H.T., Shi, H.X., Zhao, X.X.: Spectrum usage prediction based on high-order markov model for cognitive radio networks. In: IEEE 10th International Conference on Computer and Information Technology (CIT), pp. 2784–2788. Bradford, UK (2010)

    Google Scholar 

  12. Li, Z., Shi, P., Chen, W., Yan, Y.: Square law combining double threshold energy detection in Nakagami channel. Int. J. Digit. Content Technol. Appl. 5(12), (2011)

    Google Scholar 

  13. Mukherjee, A., Datta, A.: Spectrum sensing for cognitive radio using quantized data fusion and hidden Markov model. In: Proceedings of IEEE, pp. 133–137 (ISBN: 978-1-4799-2981-8/14)

    Google Scholar 

  14. Akbar, I., Tranter, W.: Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case. In: Proceedings of IEEE SoutheastCon, pp. 196–20 (2007)

    Google Scholar 

  15. Teguig, D., Scheers, B., Le Nir, V.: Data fusion schemes for cooperative spectrum sensing in cognitive radio networks. In: Communications and Information Systems Conference, pp. 1–7 (2012). ISBN: 978-1-4673-1422-0

    Google Scholar 

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Authors and Affiliations

  1. School of Electronics Engineering, KIIT University, Bhubaneswar, India

    Sagarika Sahoo, Tapaswini Samant, Amrit Mukherjee & Amlan Datta

Authors
  1. Sagarika Sahoo
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  2. Tapaswini Samant
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  3. Amrit Mukherjee
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  4. Amlan Datta
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Corresponding author

Correspondence to Sagarika Sahoo .

Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering, Oakland University, Rochester, Michigan, USA

    Ishwar K. Sethi

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Sahoo, S., Samant, T., Mukherjee, A., Datta, A. (2015). Advanced Energy Sensing Techniques Implemented Through Source Number Detection for Spectrum Sensing in Cognitive Radio. In: Sethi, I. (eds) Computational Vision and Robotics. Advances in Intelligent Systems and Computing, vol 332. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2196-8_21

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  • DOI: https://doi.org/10.1007/978-81-322-2196-8_21

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2195-1

  • Online ISBN: 978-81-322-2196-8

  • eBook Packages: EngineeringEngineering (R0)

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