Waveform Classification in Radar-Communications Coexistence Scenarios

Gyuyeol Kong, Minchae Jung, Visa Koivunen

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

6 Citations (Scopus)
139 Downloads (Pure)


In this paper the problem of recognizing waveform and modulation is addressed in radar-communications coexistence and shared spectrum scenarios. We propose a deep learning method for waveform classification. A hierarchical recognition approach is employed. The received complex-valued signal is first classified to single carrier radar, communication or multicarrier waveforms. Fourier synchrosqueezing transformation (FSST) time-frequency representation is computed and used as an input to a convolutional neural network (CNN). For multicarrier signals, key waveform parameters including the cyclic prefix (CP) duration, number of subcarriers and subcarrier spacing are estimated. The modulation type used for subcarriers is recognized. Independent component analysis (ICA) is used to enforce independence of I- and Q-components, and consequently significantly improving the classification performance. Simulation results demonstrate the high classification performance of the proposed method even for orthogonal frequency division multiplexing (OFDM) signals with high-order quadrature amplitude modulation (QAM).

Original languageEnglish
Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
Number of pages6
ISBN (Electronic)9781728182988
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Global Communications Conference - Taipei, Taiwan, Republic of China
Duration: 7 Dec 202011 Dec 2020

Publication series

NameIEEE Global Communications Conference
ISSN (Print)2334-0983
ISSN (Electronic)2576-6813


ConferenceIEEE Global Communications Conference
Abbreviated titleGLOBECOM
Country/TerritoryTaiwan, Republic of China


  • convolutional neural network
  • Fourier synchrosqueezing transform
  • independent component analysis
  • Signal intelligence
  • waveform recognition


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