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Adaptive inverse control system can improve the performance of turbo decoding,and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is proposed in this paper. Compared with linear filter with its revi-sion,the general relationship between the input and output of the inverse model of turbo decoding system can be established exactly by Nonlinear Auto-Regressive eXogeneous input (NARX) filter. Combined with linear inverse system,it has simpler structure and costs less computation,thus can satisfy the demand of real-time turbo decoding. Simulation results show that neural network in-verse control system can improve the performance of turbo decoding further than other linear con-trol system.
Adaptive inverse control system can improve the performance of turbo decoding, and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is proposed in this paper. Compared with linear filter with its revi-sion , the general relationship between the input and output of the inverse model of turbo decoding system can be set of exactly nonlinearizer-regressive eXogeneousinput (NARX) filter. combined with linear inverse system, it has simpler structure and costs less computation, thus can satisfy the demand of real-time turbo decoding. Simulation results show that neural network in-verse control system can improve the performance of turbo decoding further than other linear con-trol system.