ML decoding for convolutional code for short codeword of short constraint length and alternate use of block code

This paper primarily deals with the error correction for the error correcting code, convolutional code. Viterbi decoding algorithm is the well known algorithm to decode convolutional code. Some of its limitations are overcome by the proposed algorithm in (Saifullah and Al-Mamun, 2004). This paper shows the improvement made by maximum likelihood (ML) decoding in simple form over the Viterbi algorithm and the proposed algorithm in (Saifullah and Al-Mamun, 2004) for short codeword and constraint length because of its low complexity. With this ML decoding, alternate use of block and convolutional code saves receiver´s decoding power as well as computational complexity.