DOI QR코드

DOI QR Code

Matching Pursuit Based Sparse Multipath Channel Estimation for Multicarrier Systems

다중반송파 시스템의 정합추구 기반 희소 다중경로 채널 추정

  • Kim, See-Hyun (Dept. of Information and Communication Engineering, Suwon University)
  • 김시현 (수원대학교 정보통신공학과)
  • Received : 2012.06.29
  • Accepted : 2012.09.25
  • Published : 2012.09.30

Abstract

Although linear channel estimation for the frequency selective fading channel has been widely deployed, its accuracy depends on the number of pilots to probe the channel. Thus, it is unavoidable to employ large number of pilots to enhance the channel estimation performance, which essentially leads to the degradation of the transmission efficiency. It even does not utilize the sparseness of the multipath channel. In this paper a sparse channel estimation scheme based on the matching pursuit algorithm and a pilot assignment method, which minimizes the coherence, are proposed. The simulation results reveal that the proposed algorithm shows superior channel estimation performance with fewer pilots to the LS based ones.

주파수 선택적 페이딩 채널을 위한 선형 채널 추정 방식의 성능은 파일럿의 개수에 비례하므로 채널 추정의 정확도를 높이기 위해서 많은 파일럿을 쓰지 않을 수 없으며 필연적으로 전송 효율성이 낮아지는 단점이 있다. 또한 다중경로 채널의 희소(sparse)한 특성을 활용하지 않고 있다. 본 논문에서는 압축센싱 기법을 이용하여 아주 적은 수의 파일럿으로 희소한 채널을 추정하는 정합추구 기반 알고리듬과 간섭도(coherence)를 최소화하기 위한 파일럿 배치 방법을 제안한다. 또한 모의 실험을 통해 LS (least square) 채널 추정 방식보다 적은 수의 파일럿으로 우수한 채널 추정 성능을 보임을 확인한다.

Keywords

References

  1. ETSI EN 300 744: Digital Video Broadcasting (DVB); Framing structure, channel coding and modulation for digital terrestrial television, v.1.5.1, 2004-11
  2. M. Kocic, D. Brady and M. Stojanovic, "Sparse equalization for real-time digital underwater acoustic communications," Proc. OCEANS '95, pp. 1417-1422.
  3. D. Donoho, "Compressed sensing," IEEE Trans., Information Theory, vol. 52, no. 4, pp. 1289-1306, Apri, 2006. https://doi.org/10.1109/TIT.2006.871582
  4. E. Candes, J. Romberg, and T. Tao, " Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Information Theory, vol. 52, no. 2, pp. 489-509, February, 2006. https://doi.org/10.1109/TIT.2005.862083
  5. E. Candes and T. Tao, "Decoding by linear programming," IEEE Trans. Information Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005. https://doi.org/10.1109/TIT.2005.858979
  6. D. Donoho and X. Huo, "Uncertainty principles and ideal atomic decomposition," IEEE Trans. Information Theory, vol. 47, no. 7, pp. 2845-2862, November, 2001. https://doi.org/10.1109/18.959265
  7. J. A. Tropp, "Greed is good: algorithmic results for sparse approximation," IEEE Trans. Information Theory, vol. 50, no. 10, pp. 2231-2242, Oct. 2004. https://doi.org/10.1109/TIT.2004.834793
  8. D. Needell and J. A. Tropp, "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples," Appl. Comput. Harmon.Anal., vol 26, no. 3, pp. 301-321, May 2009 https://doi.org/10.1016/j.acha.2008.07.002
  9. D. Needell and R. Vershynin, "Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit," IEEE Trans. J. Selected Topics of Signal Processing, vol. 4, no. 2, pp. 310-316, Apr. 2010 https://doi.org/10.1109/JSTSP.2010.2042412
  10. D. Donoho, Y. Tsaig I. Drori, and J. Starck, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit," IEEE Trans. Information Theory, vol. 58, no. 2, pp. 1094-1121, Feb. 2012. https://doi.org/10.1109/TIT.2011.2173241
  11. W. Dai and O. Milenkovic, "Subspace pursuit for compressive sensing signal reconstruction," IEEE Trans. Information Theory, vol. 55, no. 5, pp. 2230-2249, May. 2009. https://doi.org/10.1109/TIT.2009.2016006
  12. ETSI TR 102 377: Digital Video Broadcasting (DVB); DVB-H Implementation Guidelines, 2006