BER Analysis of Turbo Coded Channel

Turbo codes are a new class of forward error correcting codes that have proved to give a performance close to the channel capacity as proposed by C. Shannon. A turbo code encoder is formed by the parallel concatenation of two identical recursive convolutional encoders separated by an interleaver. The turbo code decoder utilizes two cascaded decoding blocks where each block in turn shares a priori information produced by the other. The decoding scheme has the benefit to work iteratively such that the overall performance can be improved. In this paper, a performance analysis of turbo codes is carried out. Two decoding techniques, namely, Log maximum a posteriori probability (Log-MAP) algorithm, and the soft output Viterbi algorithm (SOVA), which uses log-likelihood ratio to produce soft outputs are used in the performance analysis. The effect of using different decoding schemes is studied on both punctured and unpunctured codes. Finally, the performance of the two different decoding techniques is compared in terms of bit error rate. Simulations are carried out with the help of MATLAB tools. Results indicate that turbo codes outperform the well-accepted convolutional codes with same constraints, however the best results are given by the unpunctured Log-MAP decoding of turbo codes.