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Compressive Spectrum Sensing Based on Sparse Sub-band Basis in Wireless Sensor Network

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Advances in Wireless Sensor Networks (CWSN 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 501))

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Abstract

An approach based on Sparse Sub-band Basis (SSB) for compressive spectrum sensing in Wireless Sensor Network (WSN) is presented in this paper, considering the unsatisfactory accuracy and complex calculation of the traditional ones. It is proved that the SSB matches not only the orthogonality and completeness of the basis, but also the Restricted Isometry Property (RIP) in reconstructing the signal. The simulation results show that the reconstruction based on SSB can detect the accurate location and amplitude of spectrum occupancy, and is more robust than traditional edge detection method. Additionally, this approach has higher compression ratio and less calculation, which is suitable for nodes in WSN.

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References

  1. Mitola, J., Maguire, G.Q.: Cognitive radio: making software radios more personal. J. IEEE Pers. Commun. 6(4), 13–18 (1999)

    Article  Google Scholar 

  2. Bazerque, J.A., Giannakis, G.B.: Distributed spectrum sensing for cognitive radio networks by exploiting sparsity. J. IEEE Trans. Sign. Proces. 58(3), 1847–1862 (2009)

    Article  MathSciNet  Google Scholar 

  3. Granelli, F., Zhang, H., Zhou, X., Marano, S.: Research advances in cognitive ultra wideband radio and their application to sensor networks. J. Mob. Netw. Appl. 11(4), 487–499 (2006)

    Article  Google Scholar 

  4. Donoho, D.L.: Compressed sensing. J. IEEE Trans. Inf. Theo. 52(4), 1289–1306 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. Tropp, A., Laska, J.N., Duarte, M.F., Romberg, J.K., Baraniuk, R.G.: Beyond Nyquist: efficient sampling of sparse bandlimited signals. J. IEEE Trans. Inf. Theo. 56(1), 520–544 (2010)

    Article  MathSciNet  Google Scholar 

  6. Tian, Z., Giannakis, G.B.: Compressed sensing for wideband cognitive radios. In: IEEE ICASSP, pp. 1357–1360. IEEE Press, Honolulu (2007)

    Google Scholar 

  7. Yu, Z., Sebastian, H., Sadler, B.M.: Mixed-signal parallel compressed sensing and reception for cognitive radio. In: IEEE ICASSP, pp. 3861–3864. IEEE Press, Las Vegas (2008)

    Google Scholar 

  8. Amaro, J.P., Ferreira, F.J.T.E., Cortesao, R., Vinagre, N., Bras, R.P.: Low cost wireless sensor network for in-field operation monitoring of induction motors. In: IEEE ICIT, pp. 1044–1049. IEEE Press, Vi a del Mar (2010)

    Google Scholar 

  9. Donoho, D.L., Huo, X.: Uncertainty principles and ideal atomic decomposition. J. IEEE Trans. Inf. Theor. 47(7), 2845–2862 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  10. Polo, Y.L., Ying W., Pandharipande, A., Leus, G.: Compressive wide-band spectrum sensing. In: IEEE ICASSP, pp.2337–2340. IEEE Press, Taipei (2009)

    Google Scholar 

  11. Sun, X., Zhou, Z., Shi, L., Zou, W.: A novel compressed collaborative sensing scheme using LDPC technique. In: CHINACOM ICST, pp. 959–963. IEEE Press, Harbin (2011)

    Google Scholar 

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Authors and Affiliations

  1. Air and Missile Defense College, AFEU, Shaanxi Xi’an, China

    Yu Wang, Jincheng Zhang, Quan Wang, Fangxu Lv & Kewei Chen

Authors
  1. Yu Wang
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  2. Jincheng Zhang
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  3. Quan Wang
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  4. Fangxu Lv
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  5. Kewei Chen
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Corresponding author

Correspondence to Yu Wang .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Limin Sun

  2. Beijing, China

    Huadong Ma

  3. Northwest University, Xi’an, China

    Dingyi Fang

  4. Northwest University, Xi’an, China

    Jinping Niu

  5. Northwest University, Xi’an, China

    Wei Wang

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© 2015 Springer-Verlag Berlin Heidelberg

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Wang, Y., Zhang, J., Wang, Q., Lv, F., Chen, K. (2015). Compressive Spectrum Sensing Based on Sparse Sub-band Basis in Wireless Sensor Network. In: Sun, L., Ma, H., Fang, D., Niu, J., Wang, W. (eds) Advances in Wireless Sensor Networks. CWSN 2014. Communications in Computer and Information Science, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46981-1_5

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  • DOI: https://doi.org/10.1007/978-3-662-46981-1_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46980-4

  • Online ISBN: 978-3-662-46981-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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