Optimization of the Decoding Performance of Rate ⅓ Convolutional Code

Convolutional code is the most reliable error correcting code for transmitting and retrieving the error free data. Convolutional codes are widely used because of their flexibility and simplicity. In this paper, we analyze the decoding performance with respect to Shannon’s theoretical limit for a rate ⅓ convolutional code for different constraint lengths and decoding schemes like hard decision Viterbi decoding and soft decision Viterbi decoding. The decoding delay is large in Viterbi decoding algorithm used for convolutional decoding. So a method is proposed for fast decoding of convolutional codes which reduces bit error rate and decoding delay using particle swarm optimization which is an efficient optimization technique.