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