Turbo code encoder identification based on check function gradient
To overcome difficulties in poor performances of fault tolerance and high computational effort in existing turbo code encoder recognition algorithms,an encoder recognition algorithm with low computational complexity and high fault tolerance under low signal-to-noise ratio(SNR)conditions is proposed.In this paper,the linear constraint relationship among symbols is used to construct a coding cost function,and the function conformity at each moment is defined.This concept clearly shows the strength of the constraint relationship among symbols at each moment.The function conformity is used as the cost function to identify the recursive systematic convolutional(RSC)code,the probability of the generated polynomial parameter to be discerned is regarded as the variable of the cost function,and the RSC code identification problem is then transformed into solving a maximal value problem of the cost function.Finally,using iterative updating,under a fixed amount of iterations,the gradient of the un-known generator matrix parameters in the function is solved to complete the identification.The simulation results demonstrate that the proposed algorithm can recognize encoder parameters when iterating 20 times.Moreover,the computational complexity is low,and the error tolerance is stronger than those of existing algorithms in low-SNR scenarios.
万方数据
维普
