List decoding of turbo codes Academic Article uri icon

abstract

  • List decoding of turbo codes is analyzed under the assumption of a maximum-likelihood (ML) list decoder. It is shown that large asymptotic gains can be achieved on both the additive white Gaussian noise (AWGN) and fully interleaved flat Rayleigh-fading channels. It is also shown that the relative asymptotic gains for turbo codes are larger than those for convolutional codes. Finally, a practical list decoding algorithm based on the list output Viterbi algorithm (LOVA) is proposed as an approximation to the ML list decoder. Simulation results show that the proposed algorithm provides significant gains corroborating the analytical results. The asymptotic gain manifests itself as a reduction in the bit-error rate (BER) and frame-error rate (FER) floor of turbo codes. © 1998 IEEE.

altmetric score

  • 6

author list (cited authors)

  • Narayanan, K. R., & Stuber, G. L.

citation count

  • 35

publication date

  • June 1998