{"title":"Joint Demodulation and Decoding with Multi-Label Classification Using Deep Neural Networks","authors":"I. Ahmed, Wenjie Xu, R. Annavajjala, Woo-Sung Yoo","doi":"10.1109/ICAIIC51459.2021.9415182","DOIUrl":null,"url":null,"abstract":"In this paper, we leverage the power of artificial intelligence in the receiver design for joint baseband demodulation and channel decoding. We consider a point-to-point communication system and develop a deep neural network (DNN) based joint demodulator and decoder (DeModCoder) that accomplishes the tasks of demodulation and decoding in a single operational block. We incorporate a multi-label classification (MLC) scheme for the considered DNN framework, which is trained offline over a wide-range of signal-to-noise ratios (SNRs) in a supervised learning manner and deployed online in real-time applications. Simulation results demonstrate that our developed DeModCoder outperforms the conventional block-based sequential demodulation and decoding schemes. We also observe that the MLC DeModCoder shows better performance than conventional multiple output classifier in high SNR region while incurring lower computational complexity.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIIC51459.2021.9415182","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we leverage the power of artificial intelligence in the receiver design for joint baseband demodulation and channel decoding. We consider a point-to-point communication system and develop a deep neural network (DNN) based joint demodulator and decoder (DeModCoder) that accomplishes the tasks of demodulation and decoding in a single operational block. We incorporate a multi-label classification (MLC) scheme for the considered DNN framework, which is trained offline over a wide-range of signal-to-noise ratios (SNRs) in a supervised learning manner and deployed online in real-time applications. Simulation results demonstrate that our developed DeModCoder outperforms the conventional block-based sequential demodulation and decoding schemes. We also observe that the MLC DeModCoder shows better performance than conventional multiple output classifier in high SNR region while incurring lower computational complexity.