LIST DECODING OF TURBO CODES

List decoding of turbo codes is analyzed under the assumption of a max immm-likelihood (ML) list decoder, It is shown that large asymptotic g ains 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 tho se for convolutional codes. Finally, a practical list decoding algorit hm based on the list output Viterbi algorithm (LOVA) is proposed as ar e approximation to the ML list decoder. Simulation results show that t he proposed algorithm provides significant gains corroborating the ana lytical results, The asymptotic gain manifests itself as a reduction i n the bit-error rate (BER) and frame error rate (FER) floor of turbo c odes.