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 ermr propagation, finite interleaving and the length of the initial training sequence is explicitly studied. A binary Gilbert-Elliott channel is used to model the wmla t ion structure of the e m f p m e s s . Comparison of the DFD and the hard decision decoding with Hamming metric mveals a cmssover between the performance curves, depending on the interleaving parameters and channel statistics.