The technique of the convolutional turbo coding was proposed using iterative decoding, recursive systematic convolutional code, nonuniform random interleaving, soft-in soft-out concepts and the maximum a posteriori algorithm or Viterbi algorithm, the turbo coding in terms of bit error rate has been shown that have good coding gain performance. As the number if iterations increases, jointing scaling factor within the soft inputs and outputs of decoding gradually becomes more reliable. Then, in this thesis, we presents an efficient adaptive scaling scheme to normalize the extrinsic information, which is exchanged between the constituent decoders, can improve the decoding quality.