Error control systems for digital communication and storage
Error control systems for digital communication and storage
Applications of neural networks to digital communications: a survey
Signal Processing - Special issue on emerging techniques for communication terminals
Turbo codes: principles and applications
Turbo codes: principles and applications
Coding Theory: The Essentials
Novel Use of Channel Information in a Neural Convolutional Decoder
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Soft output decision convolutional (SONNA) decoders based on the application of neural networks
Engineering Applications of Artificial Intelligence
Communication Systems
Theory and application of neural networks for 1/n rate convolutional decoders
Engineering Applications of Artificial Intelligence
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A novel algorithm for decoding a general rate K/Nconvolutional code based on recurrent neural network (RNN) is described and analysed. The algorithm is introduced by outlining the mathematical models of the encoder and decoder. A number of strategies for optimising the iterative decoding process are proposed, and a simulator was also designed in order to compare the Bit Error Rate (BER) performance of the RNN decoder with the conventional decoder that is based on Viterbi Algorithm (VA). The simulation results show that this novel algorithm can achieve the same bit error rate and has a lower decoding complexity. Most importantly this algorithm allows parallel signal processing, which increases the decoding speed and accommodates higher data rate transmission. These characteristics are inherited from a neural network structure of the decoder and the iterative nature of the algorithm, that outperform the conventional VA algorithm.