Evaluation of a Reduced Complexity ML Decoding Algorithm for Tailbiting Codes on Wireless Systems
Tailbiting convolutional codes will be used for several applications in new cellular mobile radio systems. 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 highly increases the complexity of the decoding process. Recently, the use of the A algorithm has highly decreased the computational complexity of Maximum Likelihood (ML) decoding of tailbiting convolutional codes. The decoding process in this case requires two phases. In the first phase, a typical Viterbi decoding is employed to collect information regarding the trellis.