Neural network error correcting decoders for block and convolutional codes

Neural network error correcting decoders for block and convolutional codes

The use of neural networks as error correcting decoders is described. It is shown that the neural networks may offer advantages in electronic countermeasure (ECM) environments in which the convolutional design assumptions of additive white Gaussian noise (AWGN) and a binary symmetric channel (BSC) a...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Caid, W.R., Means, R.W.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Additive white noise
AWGN
Character generation
Computational modeling
Convolutional codes
Decoding
Electrochemical machining
Error correction codes
Jamming
Neural networks
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Zusammenfassung:The use of neural networks as error correcting decoders is described. It is shown that the neural networks may offer advantages in electronic countermeasure (ECM) environments in which the convolutional design assumptions of additive white Gaussian noise (AWGN) and a binary symmetric channel (BSC) are violated. Some results of preliminary studies and benefits of the neural-based decoder approach are discussed.< >
DOI:10.1109/GLOCOM.1990.116658