Gaussian function assisted neural networks decoding algorithm for turbo product codes

Authors:
Xingcheng Liu;Jinlong Cai
Affiliations:
School of Information Science and Technology, Sun Yat-sen University, Guangzhou, Guangdong, China,National Mobile Communications Research Laboratory, Southeast University, Nanjing, Jiangsu, China;School of Information Science and Technology, Sun Yat-sen University, Guangzhou, Guangdong, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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Abstract

We apply the radial basis functions (RBF) decoder adopting Gaussian function for the Turbo product codes (TPC). An extrinsic information extraction scheme based on RBF neural networks (NN) is suggested, and a novel RBF NNs decoding algorithm is proposed. The extrinsic information transfer (EXIT) charts have been used to analyze the convergence property of the TPCs. The EXIT chart analyses show that the proposed decoding algorithm could achieve convergence with about 5 iterations, and improve BER performance in low Eb/N0 regions. Simulation results show that the proposed algorithm achieves promising BER performance while decreasing decoding computation compared with the maximum a posterior (MAP) algorithm.