Classification style regression for spectral opening pmf estimation

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

Abstract

Dynamic spectrum allocation (DSA) permits unlicensed users to access spectrum owned by a licensed user given they do so without interference to the primary user. To avoid interference with other users, the unlicensed user needs to be aware of channel availability. Spectrum sensing allows a radio to find spectrum holes, but costs energy and time. Predictive methods can be used to decrease the amount of spectrum sensing needed to find an available channel. We designed a novel neural network architecture for spectrum hole prediction. This neural network is capable of creating probability mass functions (PMF) estimates of the length of channel openings with no assumptions of the initial probability distribution or prior knowledge about the traffic. This architecture is shown to work through a mathematical proof, and its performance is measured through simulation.

Original languageEnglish (US)
Title of host publication55th Annual International Telemetering Conference, ITC 2019
Subtitle of host publicationCultivating the Next Generation of Range Engineers
PublisherInternational Foundation for Telemetering
Pages66-74
Number of pages9
ISBN (Electronic)9781713801887
StatePublished - 2019
Event55th Annual International Telemetering Conference: Cultivating the Next Generation of Range Engineers, ITC 2019 - Las Vegas, United States
Duration: Oct 21 2019Oct 24 2019

Publication series

NameProceedings of the International Telemetering Conference
Volume55
ISSN (Print)0884-5123

Conference

Conference55th Annual International Telemetering Conference: Cultivating the Next Generation of Range Engineers, ITC 2019
CountryUnited States
CityLas Vegas
Period10/21/1910/24/19

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Instrumentation

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