AI-driven blind signature classification for IoT connectivity: a deep learning approach

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dc.contributor.authorPan, Jianxiong
dc.contributor.authorYe, Neng
dc.contributor.authorYu, Hanxiao
dc.contributor.authorHong, Tao
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorMumtaz, Shahid
dc.contributor.authorAl-Dulaimi, Anwer
dc.contributor.authorChih-Lin, I.
dc.date.accessioned2022-02-07T12:24:33Z
dc.date.available2022-02-07T12:24:33Z
dc.date.issued2022-01-31
dc.description.abstractNon-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly notifying the utilized NOMA signatures causes large signaling cost, blind signature classification naturally becomes a low-cost option. To accomplish signature classification for NOMA, we study both likelihood- and feature-based methods. A likelihood-based method is firstly proposed and showed to be optimal in the asymptotic limit of the observations, despite high computational complexity. While feature-based classification methods promise low complexity, efficient features are non-trivial to be manually designed. To this end, we resort to artificial intelligence (AI) for deep learning-based automatic feature extraction. Specifically, our proposed deep neural network for signature classification, namely DeepClassifier, establishes on the insights gained from the likelihood-based method, which contains two stages to respectively deal with a single observation and aggregate the classification results of an observation sequence. The first stage utilizes an iterative structure where each layer employs a memory-extended network to explicitly exploit the knowledge of signature pool. The second stage incorporates the straight-through channels within a deep recurrent structure to avoid information loss of previous observations. Experiments show that DeepClassifier approaches the optimal likelihood-based method with a reduction of 90% complexity.en_UK
dc.identifier.citationPan J, Ye N, Yu H, et al., (2022) AI-driven blind signature classification for IoT connectivity: a deep learning approach, IEEE Transactions on Wireless Communications, Volume 21, Number 8, August 2022, pp. 6033-6047en_UK
dc.identifier.eissn1558-2248
dc.identifier.issn1536-1276
dc.identifier.urihttps://doi.org/10.1109/TWC.2022.3145399
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17548
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectNon-orthogonal multiple accessen_UK
dc.subjectsignature classificationen_UK
dc.subjectdeep learningen_UK
dc.subjectrecurrent neural networken_UK
dc.subjectautomatic feature extractionen_UK
dc.titleAI-driven blind signature classification for IoT connectivity: a deep learning approachen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2022-01-14

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