TY  - JOUR
T1  - The design and performance of a neural network for predicting turbo decoding error with application to hybrid ARQ protocols.
JF  - IEEE Transactions on communications
A1  - Buckley, M.E
LA  - English
UL  - https://tuklas.up.edu.ph/Record/UP-99796217609500245
AB  - 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 the beginning or the conclusion of the decoding process, providing improved reliability and throughput performance at a lower average decoding complexity than turbo decoding with cyclic redundancy check error detection
KW  - Average decoding complexity.
KW  - Component decoder output.
KW  - Correlation.
KW  - Cross entropy.
KW  - Cyclic redundancy check error detection.
KW  - Decoded data errors.
KW  - Decoder iterations.
KW  - Feedforward hidden-layer neural networks.
KW  - Hybrid ARQ protocols.
KW  - Neural network design.
KW  - Neural network performance.
KW  - Reliability.
KW  - Retransmission requests.
KW  - Simulation results.
KW  - Throughput performance.
KW  - Turbo decoding error prediction.
KW  - Turbo error control system.
ER  -