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

The Institute of Electronics and Information Engineers (대한전자공학회)
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Density Evolution Analysis of RS-A-SISO Algorithms for Serially Concatenated CPM over Fading Channels

페이딩 채널에서 직렬 결합 CPM (SCCPM)에 대한 RS-A-SISO 알고리즘과 확률 밀도 진화 분석

  • Chung, Kyu-Hyuk (Communication Sciences Institute, Electrical Engineering-Systems, Dept. University) ;
  • Heo, Jun (College of Information and Telecommunications Department of Electronics Engineering Konkuk University)
  • 정규혁 (미국 University of Southern California 전기공학과) ;
  • 허준 (건국대학교 전자공학부)
  • Published : 2005.07.01
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Abstract

Iterative detection (ID) has proven to be a near-optimal solution for concatenated Finite State Machines (FSMs) with interleavers over an additive white Gaussian noise (AWGN) channel. When perfect channel state information (CSI) is not available at the receiver, an adaptive ID (AID) scheme is required to deal with the unknown, and possibly time-varying parameters. The basic building block for ID or AID is the soft-input soft-output (SISO) or adaptive SISO (A-SISO) module. In this paper, Reduced State SISO (RS-SISO) algorithms have been applied for complexity reduction of the A-SISO module. We show that serially concatenated CPM (SCCPM) with AID has turbo-like performance over fading ISI channels and also RS-A-SISO systems have large iteration gains. Various design options for RS-A-SISO algorithms are evaluated. Recently developed density evolution technique is used to analyze RS-A-SISO algorithms. We show that density evolution technique that is usually used for AWGN systems is also a good analysis tool for RS-A-SISO systems over frequency-selective fading channels.

Iterative detection은 additive white Gaussian noise(AWGN) channel의 경우 interleaver들을 포함한 조합유한상태머신(concatenated Finite State Machine)들에 대해 근사적으로 optimal solution에 가깝다는 것이 입증되었습니다. 수신단에서 정확한 채널 상태 정보(perfect channel state information)가 얻어질 수 없는 경우 adaptive Iterative detection이 시간적으로 변하거나 또는 부정확한 채널 변수를 다루기위해 필요합니다. Iterative detection과 adaptive iterative detection대한 기본 building block은 각각 Soft-Input Soft-Output (SISO)와adaptive SISO (A-SISO)입니다. SISO와 A-SISO의 complexity은 state memory나 channel memory에 비례해서 지수적으로 증가합니다. 본 논문에서는 Reduced State SISO (RS-SISO) 알고리즘이 A-SISO의 complexity 감소를 위해 적용되어 fading ISI channel을 통한 serially concatenated CPM의 성능이 adaptive iterative detection을 이용하면 터보 코드 같은 성능을 나타내는 것과 또한 RS-A-SISO system이 큰 iterative detection gain을 가지는 것을 보였습니다. RS-A-SISO 알고리즘에 대한 다양한 design option들의 성능을 평가하였으며 성능과 complexity를 비교하였습니다. 또한 보통 AWGN 채널에서 사용되어지는 density evolution 분석기법이 주파수 선택적인 페이딩 채널에서 RS-A-SISO 시스템에서도 좋은 분석기법임을 보였습니다

Keywords

References

  1. C. Berrou and A. Glavieux. 'Near optimum error correcting coding and decoding: turbo-codes,' IEEE Trans. commun., vol. 44, October 1996, pp. 1261-1271 https://doi.org/10.1109/26.539767
  2. L. Yim and G. L. Stuber, 'MLSE and soft-output equalization for trellis-coded continuous phase modulation,' IEEE Trans. commun., vol. 45, October 1997, pp. 651-659 https://doi.org/10.1109/26.592602
  3. K. R. Narayanan and G. L. Stuber, 'Performance of trellis coded cpm with iterative demodulation and decoding,' in Proc. Globecom Conf., Rio de Janeiro, Brazil, 1999 https://doi.org/10.1109/GLOCOM.1999.831722
  4. S. Benedetto, D. Divsalar, G. Montorsi, and F. Pollara, 'Soft-input soft-output modules for the construction and distributed iterative decoding of code networks', European Trans. Commun., February/March 1998, pp. 155-172
  5. P. Moqvist and T. Aulin, 'Power and bandwidth efficient serially concatenated cpm with iterative decoding,' in Proc. Globecom Conf., San Fransico, USA, 2000, pp. 790-794 https://doi.org/10.1109/GLOCOM.2000.891247
  6. A Anastasopoulos and K. Chugg, 'Adaptive soft-input soft-output algorithms for iterative detection with parametric uncertainty,' IEEE Trans. commun., vol.48, pp. 1638-1649, October 2000, pp. 1638-1649 https://doi.org/10.1109/26.871389
  7. X. Chen and K. Chugg, 'Reduced state soft-in/soft-out for complexity reduction in iterative and non-iterative data detection,' Proc. ICC, 2000, pp. 6-10
  8. G. Colavolpe, G. Ferrari, and R. Raheli,'Reduced state bcjr-type algoriths,' in Proc. ICC, 2000, pp. 460-464
  9. T. Richardson and R. Urbanke,'The capacith of low density parity check codes under message passing decoding,' IEEE Trans. Inform. Theory, vol.47, February 2001, pp. 599-618 https://doi.org/10.1109/18.910577
  10. S. Chung, T. Richardson, and R. Urbanke, 'Analysis of sum-product decoding of low density parity check codes using a gaussian approximation,' IEEE Trans. Inform. Theory, vol.47, February 2001, pp. 657-670 https://doi.org/10.1109/18.910580
  11. H. E. Gamal and A. R. Hammons, Jr., 'Analyzing the turbo decoder using the gaussian approximation,' IEEE Trans. Inform. Theory, vol.47, February 2001, pp. 671-686 https://doi.org/10.1109/18.910581
  12. D. Divsalar, S. Dolinar, and F. Pollara, 'Iterative turbo decoder analysis based on density evolution,' IEEE J. Select. Areas Commun., vol. 19, May 2001, pp. 891-907 https://doi.org/10.1109/49.924873
  13. K. Chung, J. Heo, and K. M. Chugg, 'Reduced state adaptive siso algorithms for serially concatenated cpm over frequency-selective fading channels,' in Proc, Globecom Conf., San Antonio, USA, 2001, pp. 1162-116 https://doi.org/10.1109/GLOCOM.2001.965665
  14. J. Heo, K. M. Chugg, and A. Anastasopoulos, 'A comparison of forward-only and bi-directional fixed-lag adaptive sisos,' in Proc. ICC, 2000, pp. 1660-1664 https://doi.org/10.1109/ICC.2000.853776
  15. J. Heo and K. M. Chugg, 'Analysis of SCCC and LDPC code based on density evolution using a gaussian approximation,' submitted IEEE Trans. Commun., 2002
  16. John G. Proakis, 'Digital Communications,' McGraw-Hill, 1995, pp. 795-797