Autocorrelation-based traffic pattern classification for cognitive radios

Marko Höyhtyä, Heli Sarvanko, Matinmikko Marja, Aarne Mämmelä

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

3 Citations (Scopus)


This paper proposes a autocorrelation-based method to classify traffic patterns of primary channels in cognitive radio systems to allow a more accurate prediction of the future idle times. The classification algorithm uses binary information collected by spectrum sensing. It searches periodicity from the sensed binary pattern using a discrete autocorrelation function. Errors that are caused by noise and possible false sensing reports are filtered away from the autocorrelation function. We tested the method with Pareto, Weibull, and exponentially distributed stochastic traffic, and with deterministic traffic. The proposed method finds the type of traffic with a high probability when the channels of interest include both stochastic and deterministic traffic. Stochastic traffic is always classified right and regarding the deterministic traffic the probability of correct classification is over 95 % when the probability of missed detection or probability of false alarms is below 10 %.
Original languageEnglish
Title of host publication2011 IEEE Vehicular Technology Conference (VTC Fall)
Number of pages5
ISBN (Electronic)978-1-4244-8327-3, 978-1-4244-8326-6
Publication statusPublished - 2011
MoE publication typeA4 Article in a conference publication
Event74th IEEE Vehicular Technology Conference, VTC Fall 2011 - San Francisco, United States
Duration: 5 Sep 20118 Sep 2011
Conference number: 74


Conference74th IEEE Vehicular Technology Conference, VTC Fall 2011
Abbreviated titleVTC Fall 2011
CountryUnited States
CitySan Francisco


  • learning
  • predictive channel selection
  • dynamic spectrum access
  • sensors
  • traffic control
  • filtering algorithms
  • primary channels
  • Pareto distribution

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