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This paper presents TurboNet, a novel model-driven deep learning (DL)architecture for turbo decoding that combines DL with the traditionalmax-log-maximum a posteriori (MAP) algorithm. To design TurboNet, we unfold theoriginal iterative structure for turbo decoding and replace each iteration by adeep neural network (DNN) decoding unit. In particular, the DNN decoding unitis obtained by parameterizing the max-log-MAP algorithm rather than replace thewhole decoder with a black box fully connected DNN architecture. With theproposed architecture, the parameters can be efficiently learned from trainingdata, and thus TurboNet learns to appropriately use systematic and parityinformation to offer higher error correction capabilities and decreasecomputational complexity compared with existing methods. Furthermore,simulation results prove TurboNet's superiority in signal-to-noise ratiogeneralizations.

TurboNet: A Model-driven DNN Decoder Based on Max-Log-MAP Algorithm for Turbo Code