Parallel concatenated spa ce time trellis code modulation, called Turbo STCM, can efficiently increase the coding gains of the space time codes. However, the complexity of the iterat iv e decoding restricts its ap...Parallel concatenated spa ce time trellis code modulation, called Turbo STCM, can efficiently increase the coding gains of the space time codes. However, the complexity of the iterat iv e decoding restricts its application. This paper introduces a lower complex deco ding algorithm based on soft output Viterbi algorithm (SOVA) for Turbo STCM. S imulational results show that the new SOVA algorithm for the Turbo STCM outperf orms the original space time trellis code (STTC) by 4~6 dB. At the same time, compared with the Max Log MAP (maximum a posteriori) algorithm, the new scheme requires a lower complexity and approaches the performance of Turbo STCM decod ing w ith Max Log MAP.展开更多
This study proposes a simple scaling factor approach to improve the performance of parallel-concatenated convolutional code (PCCC) and serial concatenated convolutional code (SCCC) systems based on suboptimal soft-inp...This study proposes a simple scaling factor approach to improve the performance of parallel-concatenated convolutional code (PCCC) and serial concatenated convolutional code (SCCC) systems based on suboptimal soft-input soft-output (SISO) decoders. Fixed and adaptive scaling factors were estimated to mitigate both the optimistic nature of a posteriori information and the correlation between intrinsic and extrinsic information produced by soft-output Viterbi (SOVA) decoders. The scaling factors could be computed off-line to reduce processing time and implementation complexity. The simulation results show a significant improvement in terms of bit-error rate (BER) over additive white Gaussian noise and Rayleigh fading channel. The convergence properties of the suggested iterative scheme are assessed using the extrinsic information transfer (EXIT) chart analysis technique.展开更多
文摘Parallel concatenated spa ce time trellis code modulation, called Turbo STCM, can efficiently increase the coding gains of the space time codes. However, the complexity of the iterat iv e decoding restricts its application. This paper introduces a lower complex deco ding algorithm based on soft output Viterbi algorithm (SOVA) for Turbo STCM. S imulational results show that the new SOVA algorithm for the Turbo STCM outperf orms the original space time trellis code (STTC) by 4~6 dB. At the same time, compared with the Max Log MAP (maximum a posteriori) algorithm, the new scheme requires a lower complexity and approaches the performance of Turbo STCM decod ing w ith Max Log MAP.
文摘This study proposes a simple scaling factor approach to improve the performance of parallel-concatenated convolutional code (PCCC) and serial concatenated convolutional code (SCCC) systems based on suboptimal soft-input soft-output (SISO) decoders. Fixed and adaptive scaling factors were estimated to mitigate both the optimistic nature of a posteriori information and the correlation between intrinsic and extrinsic information produced by soft-output Viterbi (SOVA) decoders. The scaling factors could be computed off-line to reduce processing time and implementation complexity. The simulation results show a significant improvement in terms of bit-error rate (BER) over additive white Gaussian noise and Rayleigh fading channel. The convergence properties of the suggested iterative scheme are assessed using the extrinsic information transfer (EXIT) chart analysis technique.
