Deep Learning based Channel Code Recognition using TextCNN

Xiongfei Qin, Shengliang Peng, Xi Yang, Yu Dong Yao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

The recognition of channel code of primary user signal is a important task for the full awareness of wireless environment in cognitive radio. Previous solutions to this problem usually suffer from high computational complexity that is not suitable for real-time applications and manual feature extraction that requires experience and expertise. This paper proposes a deep learning based channel code recognition algorithm that extracts features automatically and avoids complicated calculation. Three convolutional codes are considered as the candidate codes. To recognize which channel code has been adopted by the primary user, the received sequence is regarded as a text sentence and then understood by TextCNN. Experimental results show that the proposed algorithm works well and outperforms the max-log-MAP decoding algorithm in recognition accuracy.

Original languageEnglish
Title of host publication2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019
ISBN (Electronic)9781728123769
DOIs
StatePublished - Nov 2019
Event2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019 - Newark, United States
Duration: 11 Nov 201914 Nov 2019

Publication series

Name2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019

Conference

Conference2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019
Country/TerritoryUnited States
CityNewark
Period11/11/1914/11/19

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