Aiming at the symbol recovery problem at the receiver side for the special natural redun-dancy sources of non-uniform non-memory sources,a neural network decoder architecture is proposed,which is based on the fully connected neural network model.The architecture incorporates the signal-to-noise ratio of the received signal and the symbol distribution of the memoryless source along with the received data as inputs to the model.An iterative decoding algorithm based on this neural network model is proposed to realize the natural redundancy decoding in the case of unknown distributions of transmitted symbols.The simulation results show that the symbol detection performance at the receiver side can be improved by using natural redundancy.Moreover,the optimal performance can be theoreti-cally obtained by the proposed algorithm,even when the source distribution is unknown.
国家科技期刊平台
NSTL
万方数据
