The design and performance of a neural network for predicting turbo decoding error with application to hybrid ARQ protocols.

The design and performance of a neural network for predicting turbo decoding error with application to hybrid ARQ protocols.

It is shown that a neural network can be trained to observe the cross entropy of the outputs of component decoders in a turbo error control system and to accurately predict the presence of errors in the decoded data. The neural network can be used as a trigger for retransmission requests at either t...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE Transactions on communications 48, 4 (2000).
1. Verfasser: Buckley, M.E
Format: Artikel
Sprache:English
Schlagworte:
Average decoding complexity.
Component decoder output.
Correlation.
Cross entropy.
Cyclic redundancy check error detection.
Decoded data errors.
Decoder iterations.
Feedforward hidden-layer neural networks.
Hybrid ARQ protocols.
Neural network design.
Neural network performance.
Reliability.
Retransmission requests.
Simulation results.
Throughput performance.
Turbo decoding error prediction.
Turbo error control system.