An Advancing Temporal Convolutional Network for 5G Latency Services via Automatic Modulation Recognition

Yuqing Xu*, Guangxia Xu, Chuang Ma, Zeliang An

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

11 Citations (Scopus)

Abstract

Automatic modulation recognition (AMR) has received significant attention since its decisive factor for modern non-cooperative communication systems. Meanwhile, the existing works on deep learning technique achieve exceptional accuracy; however, these works dissatisfy real-time requirements for 5G low-latency services. To remedy this flaw, this letter proposes a low-latency AMR method by applying temporal convolutional network (TCN). Furthermore, the principal component analysis (PCA)-based TCN and uniform subsampling-based TCN methods are leveraged to further alleviate the computation complexity and render real-time TCN viable. Experimental results demonstrate that the proposed method can achieve lower complexity and superior recognition accuracy than existing works and pave the way for 5G low-latency services.

Original languageEnglish
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume69
Issue number6
Pages (from-to)3002 - 3006
Number of pages5
ISSN1549-7747
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • 5G
  • Automatic modulation recognition
  • deep learning
  • low-latency.
  • temporal convolutional network

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