The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Binomial Filters for Spectrum Sensing in Cognitive Radio System

인지 무선 시스템에서 스펙트럼 센싱을 위한 이항 필터

  • Received : 2014.10.08
  • Accepted : 2014.11.07
  • Published : 2014.11.28
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Abstract

In this paper, we proposed three types of binomial filter for spectrum sensing in cognitive radio system. Three filters are binomial, negative binomial and composite binomial filters and the frequency responses of their transfer functions are analyzed and the numbers of stages to meet the required attenuation are driven. As a result of performance analysis in terms of the number of stages, negative and composite binomial filters are superior to the binomial filter. Since the proposed three filters have a unified cascaded structure and are easy to be implemented without any multiplier, it is expected that they will have wide applications.

본 논문에서는 인지 무선통신에서 스펙트럼 센싱을 위해 사용할 수 있는 세 가지 형태의 이항 필터를 제안하였다. 세 가지 필터는 이항 필터 및 부적 이항필터, 복합 이항 필터이며 이들 전달함수의 주파수 응답을 각각 분석하였으며 필터에 요구되는 감쇠를 위해 필요한 단계 수를 도출하였다. 또한 각 필터에 대해 요구되는 감쇠에 필요한 단계수를 성능으로 하여 분석한 결과 부적 이항 필터 및 복합 이항 필터가 기본 이항 필터에 비해 성능이 더 우수한 것으로 나타났다. 제안된 세 가지 필터는 통합된 직렬 구조를 가지고 있고 곱셈기가 필요 없어 구현에 용이하므로 광범위한 응용이 가능하다.

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References

  1. K. Chen and T. Chiueh, "A cognitive radio system using discrete wavelet multitone modulation," IEEE Trans. Circuits and Syst.- I: Regular Papers, vol. 55, no. 10, pp. 3246-3258, Nov. 2008. https://doi.org/10.1109/TCSI.2008.924119
  2. S. Lee, J. Choi, and W. Lee, "Multi-band CIC filter based low complexity spectrum sensing method," J. KICS, vol. 34, no. 10, pp. 992-1000, 2009.
  3. X. Liu and H. Liu, "A new half-overlap filtered multitone modulation based on CIC filter in the context of cognitive radio," Int. Conf. E-Health Netw., Digital Ecosyst. Technol., pp. 276-279, Shenzhen, China, Apr. 2010.
  4. H. Jung, S. Lim, and M. Song, "Sequential spectrum sensing algorithm utilizing DFT," J. KICS, vol. 35, no. 5, pp. 490-495, 2010.
  5. H. Jung, K. Kim, and Y. Shin, "Cooperative bayesian compressed spectrum sensing for correlated in cognitive radio networks," J. KICS, vol. 38B, no. 09, pp. 765-774, 2013. https://doi.org/10.7840/kics.2013.38B.9.765
  6. Overview of Binomial Filters, http://www.cse. yorku.ca/-kosta/CompVis_Notes/binomial_filters.pdf
  7. M. Aubury, and L. Wayne, "Binomial filters," J. VLSI Signal Process., vol. 12, no. 1, pp. 35-50, Jan. 1996. https://doi.org/10.1007/BF00936945
  8. R. Haddad, "A class of orthogonal nonrecursive binomial filters," IEEE Trans. Audio and Electroacoustics, vol. 19, no. 4, pp. 296-304, Dec. 1971. https://doi.org/10.1109/TAU.1971.1162204
  9. G. Van der Wal and P. Burt, "A VLSI pyramid chip for multiresolution image analysis," Int. J. Comput. Vision, vol. 8, no. 3, pp. 177-189, Sept. 1992. https://doi.org/10.1007/BF00055150
  10. P. Burt, "Fast filter transforms for image processing," Computer Graphics and Image Process., vol. 16, no. 1, pp. 20-51, May 1981. https://doi.org/10.1016/0146-664X(81)90092-7
  11. R. Hummel and D. Lowe, "Computing gaussian blur," Int. Conf. Pattern Recognition, pp. 910-912, Paris, France, Oct. 1986.