GGD model of extrinsic information with dynamic parameter assignment for turbo decoder

This letter proposes a strategy using a generalized Gaussian distribution (GGD) to characterize the extrinsic information generated from the constituent maximum a posteriori (MAP) decoders in order to improve the performance of an iterative turbo decoder for finite block lengths. A matching technique based on the measured moments and distance criterion is introduced to dynamically select the appropriate parameters of the GGD model for the extrinsic information in each iteration. The simulation results indicate that the proposed strategy can offer performance gain in medium block lengths over both additive white Gaussian noise and Rayleigh-fading channels.