Sequential neural network decoder for convolutional code with large block sizes

dc.contributor.advisor Zhengdao Wang Yu, Xianhua
dc.contributor.department Electrical and Computer Engineering 2020-09-23T19:12:34.000 2021-02-25T21:37:15Z 2021-02-25T21:37:15Z Sat Aug 01 00:00:00 UTC 2020 2020-09-10 2020-01-01
dc.description.abstract <p>Due to the curse of dimensionality, the training complexity of the neural network based channel-code decoder increases exponentially along with the code word’s length. Although computation power has made significant progress, it is still hard to deal with long block length code word. In this thesis, we proposed a neural network based decoder termed as Sequential Neural Network Decoder (SNND). The SNND consists of multiple sub models, and it passes the last state of the current sub model to the following model as the initial state. The bit error rate (BER) performance of the SNND remains unchanged during the number of sub models increases, it achieves a performance closes to the performance of Viterbi soft decision under Additive white Gaussian noise (AWGN) channel. However, the SNND’s performance is found to decrease along with the modulation order increase.</p>
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dc.identifier archive/
dc.identifier.articleid 9259
dc.identifier.contextkey 19236854
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/18252
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 21:39:12 UTC 2022
dc.subject.keywords convolutional code
dc.subject.keywords deep learning
dc.subject.keywords neural network decoder
dc.title Sequential neural network decoder for convolutional code with large block sizes
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff Electrical Engineering(Communicationsand Signal Processing) thesis Master of Science
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