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

  • 제33권6A호
  • /
  • Pages.614-623
  • /
  • 2008
  • /
  • 1226-4717(pISSN)
  • /
  • 2287-3880(eISSN)
한국통신학회 (The Korean Institute of Commucations and Information Sciences)
권호 표지

적응형 검색 범위 기반 복잡도 감소 QRD-M MIMO 검출 기법

Low Complexity QRD-M MIMO Detection Algorithm Based on Adaptive Search Area

  • 김봉석 (영남대학교 정보통신공학과 광대역무선통신 연구실) ;
  • 최권휴 (영남대학교 정보통신공학과 광대역무선통신 연구실)
  • 발행 : 2008.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 MIMO 시스템을 위한 적응형 검색범위 기반 복잡도 감소 QRD-M 기법을 제안한다. 기존의 QRD-M 기법은 각 단계에서 survivor path들을 현 단계의 모든 가능한 성상도 심벌들로 확장하여 그 중 가장 작은 path metric을 가지는 M개를 선택한다. 그러나, 채널 상황에 따라 모든 심벌이 아닌 임시적으로 추정된 심벌의 이웃하는 포인트들로 그 검색 범위를 적절하게 줄인다 하더라도, 성능저하가 없음을 파악하였다. 이러한 특성을 이용하여, 본 논문에서는 작은 계산양으로도 MLD의 성능에 근접하는 새로운 기법을 제안한다. 채널의 신뢰도를 나타내는 지표 (indicator)로써, SNR값을 측정한 필요없는 단계들 간의 채널 이득의 비를 이용한다. 실험 결과에서는 제안된 기법이 Maximum Likelihood Detection (MLD)의 성능에 근접하면서, 계산양은 기존의 QRD-M 기법에 비해 확연하게 작다는 것을 보인다.

A very low complexity QRD-M algorithm based on adaptive search area is proposed for MIMO systems. The conventional QRD-M scheme extends each survivor paths to all constellation symbols at each layer and selects M paths of minimum path metrics. We found that performance will not be degraded even if we adaptively restrict the survivor path extension only to the neighboring points of temporary detection symbol according to the channel condition at each layer. By employing this feature, we propose a new QRD-M algorithm achieving the near MLD performance with a reduced complexity. We employ the channel gain ratio among the layers as a channel condition indicator, which does not require SNR estimation. The simulation results show that the proposed scheme effectively achieves near MLD performance while maintaining the overall average computation complexity much smaller than the conventional QRD-M algorithm.

키워드

참고문헌

  1. C. Jankiraman Space-Time codes and MIMO systems, Artech House, 2004, ISBN-1-58053-865-7
  2. E. Viterbo and J. Boutros, 'A Universal Lattice Decoder for Fading Channels,' IEEE Trans. Inform. Theory, Vol.45, pp.1639-1642, Jul. 1999 https://doi.org/10.1109/18.771234
  3. J.Yin, H.N Lee, M.Ahmed, B.Ryu and Lewis Peterson, 'Iterative MMSE-Sphere List Detection and Graph Decoding MIMO OFDM Transceiver,' IEEE Vehicular Technology Conference, pp.903-908, May 2004
  4. Wanlun Zhao and Georgios B. Giannakis, 'Sphere Decoding Algorithms With Improved Radius Search' IEEE Trans. on Commun, Vol.53, No.7, pp.1104-1109, Jul. 2005 https://doi.org/10.1109/TCOMM.2005.851590
  5. Joakim Jalden and Bjorn Ottersten, 'An Exponential Lower Bound on the Expected Complexity of Sphere Decoding,' IEEE International Conference on Acoustics, Speech and Signal Processing, IV-393-396, May 2004
  6. Mohamed Oussama Damen, Hesham El Gamal and Giuseppe Caire, 'On Maximum-Likelihood Detection and the Search for the Closest Lattice Point,' IEEE Trans. Inform. Theory, Vol.49, pp.2389-2402, Oct. 2003 https://doi.org/10.1109/TIT.2003.817444
  7. Chin, W.H.'QRD Based Tree Search Data Detection for MIMO Communication Systems', Vehicular Technology Conference, 2005. VTC 2005-Spring, 2005 IEEE 61st, 1624-1627, Jun. 2005
  8. W. Peing, S. Ma, T. Sang. N, and J. Z. Wang, 'Adaptive QRD-M Detection with Variable Number of Surviving Paths for MIMO Systems', International Symposium on Commun. and Inform. Technologies, 2007. ISCIT 2007, pp.403-408, Oct. 2007
  9. T. Im, J. Kim, and Y. Cho, 'A Low Complexity QRM-MLD for MIMO Systems', Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th. VTC 2007, pp.2243-2247, Apr. 2007
  10. H. Kawai, K. Higuchi, N. Maeda, and M. Sawahashi, 'Adaptive Control of Surviving Symbol Replica Candidates in QRM-MLD for OFDM MIMO Multiplexing,' IEEE J. Sel. Areas Commun., Vol.24, No.6, pp.1130-1140, Jun. 2006 https://doi.org/10.1109/JSAC.2005.864027
  11. K. J. Kim, J. Yue. R.A, Iltis J.D.Gibson IEEE 'A QRD-M/Kalman Filter-Based Detection and Channel Estimation Algorithm for MIMO-OFDM Systems' IEEE Trans. on Wireless Commun, Vol.4, No.2, pp.710-721, Mar. 2005 https://doi.org/10.1109/TWC.2004.842951
  12. B. Kim, and K. Choi, 'Adaptive K-best Algorithm Based on Path Metric Comparison for MIMO Systems,' The Journal of Korea Inform. and Commun. Society, Vol.32, No.11, pp.1197-1205 Nov. 2007
  13. B. Kim, H. Kim, and K. Choi, 'Path metric Comparison-based Adaptive QRD-M Algorithm for MIMO Systems,' URL: http://bwc.yu.ac.kr/080411.pdf, submitted to the Journal of KICS
  14. J. Kim, J. Kang, and C. Lee 'An Adaptive K-best Detection Algorithm for MIMO Systems,' Journal of the Institute of Electronics Engineers of Korea, vol 43-TC, N0.10, Oct. 2006
  15. David. L. Milliner and John. R. Barry 'A Layer-Adaptive M algorithm for Multiple-Input Multiple-Output Channel Detection,' IEEE Globcom 2007, Jun. 2007
  16. P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela, 'V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel,' in Proc. URSI ISSSE, 1998, pp.295-300, Oct. 1998