As the demand for wireless systems increases exponentially, it has become necessary for different wireless modalities, like radar and communications systems, to share the available bandwidth. One approach to realize coexistence successfully is for each system to adopt a transmit waveform with a unique nonlinear time-varying phase function. At the receiver of the system of interest, the waveform received for processing may still suffer from low signal-to-interference-plus-noise ratio (SINR) due to the presence of the waveforms that are matched to the other coexisting systems. This paper uses a time-frequency based approach to increase the SINR of a system by estimating the unique nonlinear instantaneous frequency (IF) of the waveform matched to the system. Specifically, the IF is estimated using the synchrosqueezing transform, a highly localized time-frequency representation that also enables reconstruction of individual waveform components. As the IF estimate is biased, modified versions of the transform are investigated to obtain estimators that are both unbiased and also matched to the unique nonlinear phase function of a given waveform. Simulations using transmit waveforms of coexisting wireless systems are provided to demonstrate the performance of the proposed approach using both biased and unbiased IF estimators.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538692189
StatePublished - Feb 19 2019
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/TerritoryUnited States
CityPacific Grove


  • Coexisting systems
  • multimodal sensing
  • ridge extraction
  • synchrosqueezing
  • time-frequency

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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