An efficiently implementable maximum likelihood decoding algorithm for tailbiting codes

Convolutional tailbiting codes are widely used in mobile systems to perform error-correcting strategies of data and control information. Unlike zero tail codes, tailbiting codes do not reset the encoder memory at the end of each data block, improving the code efficiency for short block lengths. The objective of this work is to propose a low-complexity maximum likelihood decoding algorithm for convolutional tailbiting codes based on the Viterbi algorithm. The performance of the proposed solution is compared to that of another maximum likelihood decoding strategy which is based on the A* algorithm. The computational load and the memory requirements of both algorithms are also analysed in order to perform a fair comparison between them. Numerical results considering realistic transmission conditions show the lower memory requirements of the proposed solution, which makes its implementation more suitable for devices with limited resources.