Demixing structured superposition signals from periodic and aperiodic nonlinear observations

Mohammadreza Soltani, Chinmay Hegde

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We consider the demixing problem of two (or more) structured high-dimensional vectors from a limited number of nonlinear observations where this nonlinearity is due to either a periodic or an aperiodic function. We study certain families of structured superposition models, and propose a method which provably recovers the components given (nearly) m = O (s) samples where s denotes the sparsity level of the underlying components. This strictly improves upon previous nonlinear demixing techniques and asymptotically matches the best possible sample complexity. We also provide a range of simulations to illustrate the performance of the proposed algorithms.

    Original languageEnglish (US)
    Title of host publication2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1165-1169
    Number of pages5
    ISBN (Electronic)9781509059904
    DOIs
    StatePublished - Mar 7 2018
    Event5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada
    Duration: Nov 14 2017Nov 16 2017

    Publication series

    Name2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
    Volume2018-January

    Other

    Other5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
    CountryCanada
    CityMontreal
    Period11/14/1711/16/17

    ASJC Scopus subject areas

    • Information Systems
    • Signal Processing

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