Coherent Signal Enumeration based on Deep Learning and the FTMR Algorithm

Dai Trong Hoang, Kyungchun Lee

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

10 Scopus citations

Abstract

This work explores the potential of a deep learning-aided detector for narrowband signal enumeration in a coherent environment. Specifically, we introduce the logarithmic eigenvalue-based classification network (LogECNet) to detect the signal number. In the proposed scheme, the full-row Toeplitz matrices reconstruction (FTMR) algorithm is employed to avoid the rank loss of the signal covariance matrix (SCM) in highly correlated signal environments. The simulation results show that the FTMR method not only achieves the complexity reduction with respect to the prior forward/backward spatial smoothing (FBSS) algorithm, but also improves the signal number detection performance when combined with LogECNet.

Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5098-5103
Number of pages6
ISBN (Electronic)9781538683477
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: 16 May 202220 May 2022

Publication series

NameIEEE International Conference on Communications
Volume2022-May
ISSN (Print)1550-3607

Conference

Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period16/05/2220/05/22

Keywords

  • deep neural network
  • Source number detection
  • Toeplitz matrix
  • uniform linear array

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