Decision Aided Hybrid MMSE/SIC Multiuser Detection: Structure and AME Performance Analysis*This work is supported in part by National Science Council of R.O.C. under contract number NSC92-2219-E-011-006.

  • Authors:
  • Hoang-Yang Lu;Wen-Hsien Fang

  • Affiliations:
  • The authors are with the Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C. E-mail: whf@et.ntust.edu.tw;The authors are with the Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C. E-mail: whf@et.ntust.edu.tw

  • Venue:
  • IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
  • Year:
  • 2006

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Abstract

This paper presents a simple, yet effective hybrid of the minimum mean square error (MMSE) multi-user detection (MUD) and successive interference cancellation (SIC) for direct-sequence code division multiple access (DS-CDMA) systems. The proposed hybrid MUD first divides the users into groups, with each group consisting of users with a close power level. The SIC is then used to distinguish users among different groups, while the MMSE MUD is used to detect signals within each group. To further improve the performance impaired by the propagation errors, an information reuse scheme is also addressed, which can be used in conjunction with the hybrid MMSE/SIC MUD to adequately cancel the multiple access interferences (MAIs) so as to attain more accurate detections. Furthermore, the asymptotic multiuser efficiency (AME), a measure to characterize the near-far resistance capability, is also conducted to provide further insights into the new detectors. Furnished simulations, in both additive white Gaussian noise (AWGN) channels and slow flat Rayleigh fading channels, show that the performances of the proposed hybrid MMSE/SIC detectors, with or without the decision aided scheme, are superior to that of the SIC and, especially, the one with decision aided is close to that of the MMSE MUD but with substantially lower computational complexity.