Reduced-Complexity Wrap-Around Viterbi Algorithms for Decoding Tail-Biting Convolutional Codes

Abstract:
Tail-biting convolutional codes (TBCC) are simple and powerful forward error correction (FEC) codes. They have been adopted as the FEC scheme for data and/or overhead channels in many wireless communications systems, including IS-54, EDGE, WiMAX and LTE. A convolutional code is called tail-biting (or circular) if the beginning state of its encoding trellis is always equal to the ending state after encoding. However, the circular state information is not available to the receiver. One important task in decoding TBCC is to estimate the circular state based on the received soft bit values. Many efficient algorithms have been proposed for decoding TBCC. Among them the wraparound Viterbi algorithm (WAVA) provides near-ML (maximum likelihood) performances with the lowest complexity. In this paper, we propose several algorithms to further reduce the average number of decoding iterations and the memory usage in WAVA. Simulation results show that the complexity reduction from our proposed algorithms comes with no or almost negligible performance degradation.