Sequential decoding using a priori information

A metric for sequential decoding, based on the well known Fano metric, is proposed. It is suitable for using a priori information about the source bit probability in addition to soft inputs. The advantage of this approach is a considerable reduction in the achievable bit error rate (BER) and the corresponding computational complexity of the sequential decoding algorithm (Pareto distribution). Furthermore, channel state information can easily be taken into account in this metric by applying log-likelihood ratios. Additional improvements are possible for systematic convolutional codes. Simulation results are presented for two different binary sources