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This paper deals with the Bayesian parameter estimation for recursive convolutional sub-codes of turbo codes. A Monte Carlo method named Gibbs sampling is employed for this problem. The involved conditional probabilities of the encoder coefficients and the coded sequences are derived. Simulation results show that the proposed algorithm improves the estimation accuracy substantially.
A new algorithm is developed for parameter estimation of the recursive systematic convolutional sub-codes of turbo codes. It is based on a least square cost function of the encoder coefficients. A simple iterative process assisted by a try-again scheme is designed for the optimization of the cost function. Simulation results show that, compared with the existing method, the new algorithm significantly...
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