Iterative Decoding of K/N Convolutional Codes based on Recurrent Neural Network with Stopping Criterion
- Publication Type:
- Conference
- Issue Date:
- 2007-03-12T22:05:29Z
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
This paper outlines a novel iterative decoding technique for a rate K/N convolutional code based on recurrent neural network (RNN) with stopping criterion. The algorithm is introduced by describing the theoretical models of the encoder and decoder. In particular this paper focuses on the investigation of a stopping criterion on the iterating procedure in order to minimize the decoding time yet still obtain an optimal BER performance. The simulation results of a rate 1/2 and 2/3 encoders respectively in comparison with the conventional Viterbi decoder are also presented
Please use this identifier to cite or link to this item: