Title :
List decoding of turbo codes
Author :
Narayanan, Krishna R. ; Stüber, Gordon L.
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
fDate :
6/1/1998 12:00:00 AM
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
Keywords :
Gaussian noise; Rayleigh channels; Viterbi decoding; coding errors; concatenated codes; convolutional codes; error statistics; fading; interleaved codes; maximum likelihood decoding; white noise; AWGN; BER; additive white Gaussian noise; asymptotic gain; bit-error rate; convolutional codes; error rate reduction; frame-error rate; interleaved flat Rayleigh-fading channels; list decoding algorithm; list output Viterbi algorithm; maximum-likelihood list decoder; parallel concatenated convolutional codes; simulation results; turbo codes; AWGN; Additive white noise; Analytical models; Approximation algorithms; Bit error rate; Convolutional codes; Maximum likelihood decoding; Rayleigh channels; Turbo codes; Viterbi algorithm;
Journal_Title :
Communications, IEEE Transactions on
