Preprocessing for an efficient decoding of turbo-codes with non-binary Belief Propagation

In this paper, we present an effective approach to decode turbo-codes using a non-binary Belief Propagation decoder. The proposed approach can be decomposed into two main steps: first, a non binary Tanner graph representation of the turbo-code is derived by clustering the binary parity-check matrix of the turbo-code. Then, a group Belief Propagation decoder runs several iterations on the obtained non-binary Tanner graph. We show in particular that it is necessary to add a preprocessing step on the parity-check matrix of the turbo-code in order to ensure good topological properties of the Tanner graph, and then good iterative decoding performance. Finally, by capitalizing on the diversity which comes from the existence of distinct efficient pre-processings, we propose a new decoding strategy, called decoder diversity, that intends to take benefits from the diversity through collaborative decoding schemes.