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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Liu, X., Zhang, S., Deng, Z. (2009). Study on the GA-Based Decoding Algorithm for Convolutional Turbo Codes. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_60
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DOI: https://doi.org/10.1007/978-3-642-01510-6_60
Publisher Name: Springer, Berlin, Heidelberg
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