Classification-based predictive channel selection for cognitive radios

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

46 Citations (Scopus)


The proposed method classifies traffic patterns of primary channels in cognitive radio systems and applies different prediction rules to different types of traffic. This allows a more accurate prediction of the idle times of primary channels. An intelligent channel selection scheme then uses the prediction results to find the channels with the longest idle times for secondary use. We tested the method with Pareto and exponentially distributed stochastic traffic and with deterministic traffic. The predictive method using past information improves the throughput of the system compared to a system based on instantaneous idle time information. The classification-based predictive method improves the performance compared to pure prediction when the channels of interest include both stochastic and deterministic traffic. The amount of collisions with a primary user can drop 60% within a given interval compared to a predictive system operating without classification.
Original languageEnglish
Title of host publication2010 IEEE International Conference on Communications
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)978-1-4244-6404-3, 978-1-4244-6403-6
ISBN (Print)978-1-4244-6402-9
Publication statusPublished - 2010
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Communications, ICC 2010 - Cape Town, South Africa
Duration: 23 May 201027 May 2010


ConferenceIEEE International Conference on Communications, ICC 2010
Abbreviated titleICC 2010
CountrySouth Africa
CityCape Town


  • cognitive radio
  • traffic control
  • prediction methods
  • analytical models
  • throughput
  • stochastic processes
  • microelectronics

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