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 E b/N 0 regions. Simulation results show that the proposed algorithm achieves promising BER performance while decreasing decoding computation compared with the maximum a posterior (MAP) algorithm.
This work was supported by the National Natural Science Foundation of China (No.60970041 and No. 61173018), the Science and Technology Plan of Guangzhou City of China (No.2012J4300032), and the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2011D09).
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Liu, X., Cai, J. (2013). Gaussian Function Assisted Neural Networks Decoding Algorithm for Turbo Product Codes. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_70
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DOI: https://doi.org/10.1007/978-3-642-39068-5_70
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