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Towards wireless environment cognizance through incremental learning

  • Aniqua Baset
  • , Christopher Becker
  • , Kurt Derr
  • , Samuel Ramirez
  • , Sneha Kasera
  • , Aditya Bhaskara

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

6 Scopus citations

Abstract

With the tremendous increase in the use of wireless devices, understanding the surrounding wireless/RF environment is becoming essential for many application areas. In this work, we develop the technical building blocks needed for a spectrum monitoring system that can incrementally learn about the signals present in a deployed environment. We achieve 'incremental learning (IL)' by identifying and grouping the new/unknown signals and, automatically building new machine learning (ML) models for detecting them. A thorough evaluation of our approach demonstrates its adaptability and high accuracy with signal data from several over-the-air scenarios.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages256-264
Number of pages9
ISBN (Electronic)9781728146010
DOIs
StatePublished - Nov 2019
Event16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019 - Monterey, United States
Duration: Nov 4 2019Nov 7 2019

Publication series

NameProceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019

Conference

Conference16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019
Country/TerritoryUnited States
CityMonterey
Period11/4/1911/7/19

Keywords

  • Unknown signal detection
  • Wireless signal classification

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