대한전자공학회논문지TC (Journal of the Institute of Electronics Engineers of Korea TC)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

Super-RENS 디스크 채널 모델링에서 CS-기반 Sparse Volterra 필터의 적용

Application of the CS-based Sparse Volterra Filter to the Super-RENS Disc Channel Modeling

  • 문우식 (숭실대학교 정보통신전자공학부) ;
  • 박세황 (숭실대학교 정보통신전자공학부) ;
  • 임성빈 (숭실대학교 정보통신전자공학부)
  • Moon, Woo-Sik (School of Electronic Engineering, Soongsil University) ;
  • Park, Se-Hwang (School of Electronic Engineering, Soongsil University) ;
  • Im, Sung-Bin (School of Electronic Engineering, Soongsil University)
  • 투고 : 2012.04.02
  • 심사 : 2012.05.12
  • 발행 : 2012.05.25
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초록

본 논문에서는 super-RENS 디스크의 채널 모델링을 위하여 압축 센싱 알고리즘에 기반한 sparse Volterra 필터에 대해 연구하였다. Super-RENS 디스크 시스템에서 심한 비선형 심벌간 간섭(ISI)이 발생하는 것은 익히 알려진 사실이다. 메모리를 가진 비선형 시스템은 Volterra 급수로 모델링할 수 있다. 또한, 압축 센싱은 측정치로부터 성긴 또는 압축된 신호를 복원할 수 있다. 이러한 이유로 super-RENS의 성긴 특성을 갖는 read-out 채널을 예측하기 위해 압축 센싱 알고리즘을 사용하였다. 평가 결과는 압축 센싱 알고리즘으로 super-RENS의 read-out 채널을 위한 sparse Volterra 모델을 효과적으로 구성할 수 있음을 보여준다.

In this paper, we investigate the compressed sensing (CS) algorithms for modeling a super-resolution near-field structure (super-RENS) disc system with a sparse Volterra filter. It is well known that the super-RENS disc system has severe nonlinear inter-symbol interference (ISI). A nonlinear system with memory can be well described with the Volterra series. Furthermore, CS can restore sparse or compressed signals from measurements. For these reasons, we employ the CS algorithms to estimate a sparse super-RENS read-out channel. The evaluation results show that the CS algorithms can efficiently construct a sparse Volterra model for the super-RENS read-out channel.

키워드

참고문헌

  1. J. Tominaga, T. Nakano, and N. Atoda, "An approach for recording and readout beyond the diffraction limit with an Sb thin film," Appl. Phys. Lett., vol. 73, pp. 2078-2080, 1998. https://doi.org/10.1063/1.122383
  2. M. Seo, S. Im, and J. Lee, "Nonlinear Equalization for Super-Resolution Near-Field Structure Discs," Jpn. J. Appl. Phys., vol. 47, no. 7, pp. 6045-6047, 2008. https://doi.org/10.1143/JJAP.47.6045
  3. E. J. Candès and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Process. Mag., vol. 25, no.2, pp. 21-30, Mar. 2008. https://doi.org/10.1109/MSP.2007.914731
  4. D. Donoho, "Compressed sensing," IEEE Trans. Inform. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
  5. E. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
  6. D. L. Donoho and M. Elad, "Optimally sparse representation from overcomplete dictionaries via ${\ell}_1$ norm minimization," Proc. Natl. Acad. Sci. USA, vol. 100, no. 5, pp. 2197-2002, Mar. 2002.
  7. S. G. Mallat and Z. Zhang, "Matching pursuits with time-frequency dictionaries," IEEE Trans. Signal process., vol. 41, no. 12, pp. 3397-3415, Dec. 1993. https://doi.org/10.1109/78.258082
  8. E. Candès and T. Tao, "The Dantzig selector: Statistical estimation when ${\rho}$ is much larger than $n^1$," Ann. Stat., vol. 35, no. 6, pp. 2313-2351, Dec. 2007. https://doi.org/10.1214/009053606000001523
  9. T. Blumensath and M. E. Davies, "Iterative hard thresholding for compressed sensing," Appl. Comput. Harmon. Anal., vol. 27, no. 3, pp. 265-274, 2009. https://doi.org/10.1016/j.acha.2009.04.002
  10. J. A. Tropp and A. C. Gilbert, "Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit," IEEE Trans. Inf. Theory., vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
  11. D. Needell and R. Vershynin, "Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit," IEEE J. Sel. Top. Signal Process., vol. 4, no. 2, pp. 310-316, Apr. 2010. https://doi.org/10.1109/JSTSP.2010.2042412
  12. D. Needell and J. A. Tropp, "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples," Appl. Comp. Harmonic Anal., vol. 26, no. 3, pp. 301-321, 2009. https://doi.org/10.1016/j.acha.2008.07.002
  13. K. Kwak, S. Kim, C. Lee, and K. Song, "New materials for super-resolution disc," SPIE Proceedings, vol. 6620, ODS2007 TuC5, 2007.