Analysis of soft decision decoding of interleaved convolutional codes over burst channels

We study the performance of a decision feedback decoder (DFD) for convolutional codes over an interleaved burst channel. The DFD adaptively estimates channel reliability information from previous decisions. The effect of error propagation, finite interleaving and the length of the initial training sequence is explicitly studied. A binary Gilbert-Elliott channel is used to model the correlation structure of the error process. Comparison of the DFD and the hard decision decoding with Hamming metric reveals a crossover between the performance curves, depending on the interleaving parameters and channel statistics.