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- W1549677855 abstract "A new decoding algorithm for convolutional Turbo codes that is called the Soft-Output Genetic Algorithm (SOGA) is proposed. With good individuals' diversity, wide searching region and global optimizing ability, the SOGA performs better than the Soft-Output MA in terms of BER (Bit Error Rate) with the similar complexity. Simulation results show that when 1/3 code rate, 16-state convolutional Turbo codes are decoded, at BER=10*** 5, the SOGA achieves about 0.2dB gains over the SOMA algorithm and nearly performs the same as the SOVA when BER<10*** 4. Besides, the SOGA only deals with M states in the total 2v states, so it can save 2v-M registers compared with the SOVA when 1 bit is decoded." @default.
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- W1549677855 date "2009-01-01" @default.
- W1549677855 modified "2023-09-26" @default.
- W1549677855 title "Study on the GA-Based Decoding Algorithm for Convolutional Turbo Codes" @default.
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- W1549677855 doi "https://doi.org/10.1007/978-3-642-01510-6_60" @default.
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