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• W2140791347 abstract "For the performance evaluation of convolutional codes, computer simulations are widely used, and few studies have been made for exact error probability of convolutional codes of more than 4-state. In this paper, an analytical approach is presented for moments of decision variables for a maximum a-posteriori probability (MAP) decoding. The moments are derived by recurrence relations. An application of the moments is demonstrated for error probability. In order to see a fast convergence in the moment techniques, a modified MAP is employed. The bit error probability of the modified MAP decoding is expressed by both the Gram-Charlier expansion and the maximum entropy method and the validity of the moments derivation has been confirmed." @default.
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• W2140791347 date "2011-07-01" @default.
• W2140791347 modified "2023-09-27" @default.
• W2140791347 title "Moment Analysis of Decision Variables for MAP Decoding" @default.
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• W2140791347 doi "https://doi.org/10.1109/icccn.2011.6006012" @default.
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