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IJCES
2002

Neural Network Decoders for Linear Block Codes

11 years 6 months ago
Neural Network Decoders for Linear Block Codes
This paper presents a class of neural networks suitable for the application of decoding error-correcting codes.The neural model is basically a perceptron with a high-order polynomial as its discriminant function. A single layer of high-order perceptrons is shown to be able to decode a binary linear block code with at most 2m weights in each perceptron, where m is the parity length. For some subclass codes, the number of weights needed can be much less. The (2m -1,2m -1-m) Hamming code can be decoded with only m+1 weights in each perceptron. With the help of genetic algorithms, efficient neural decoders with 2t+1 terms for each bit for some t-error correctable cyclic and BCH codes are obtained. The neural decoders are formulated as a set of parity networks in the first layer followed by a linear perceptron in the second layer, and thus have simple implementations in analogy VLSI technology.
Ja-Ling Wu, Yuen-Hsien Tseng, Yuh-Ming Huang
Added 19 Dec 2010
Updated 19 Dec 2010
Type Journal
Year 2002
Where IJCES
Authors Ja-Ling Wu, Yuen-Hsien Tseng, Yuh-Ming Huang
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