Simplified Circular Viterbi Algorithm for Tailbiting Convolutional Codes

Abstract

Tailbiting convolutional codes are used in several new cellular mobile radio systems such as WiMAX or LTE. This encoding method does not reset the encoder memory at the end of each data block, avoiding the overhead of the zero tail and improving the efficiency. Nevertheless, the absence of a known tail increases the complexity of the decoding process. Since the computational burden of maximum likelihood (ML) decoding is very high, several suboptimal algorithms have been proposed attaining good performance in terms of computational load and error correction capabilities. In this work we propose a decoding algorithm of low complexity which achieves error rates close to those of ML decoding. The proposed algorithm is based on the Circular Viterbi algorithm (CVA) and performs several iterations of the Viterbi algorithm until a tailbiting pattern is found within the path with the highest metric of the extended trellis. Simulations results obtained in an OFDM system under gaussian and wireless channels are near to the performance of the maximum-likelihood decoding and other suboptimal algorithms.