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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Distributed Channel Allocation Using Kernel Density Estimation in Cognitive Radio Networks

  • Ahmed, M. Ejaz (Department of Electronics and Radio Engineering, Kyung Hee University) ;
  • Kim, Joo Seuk (Data Intelligence Lab, SWC, Samsung Electronics) ;
  • Mao, Runkun (Department of Electrical & Computer Engineering, University of Tennessee) ;
  • Song, Ju Bin (Department of Electronics and Radio Engineering, Kyung Hee University) ;
  • Li, Husheng (Department of Electrical & Computer Engineering, University of Tennessee)
  • Received : 2011.12.21
  • Accepted : 2012.06.15
  • Published : 2012.10.31
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Abstract

Typical channel allocation algorithms for secondary users do not include processes to reduce the frequency of switching from one channel to another caused by random interruptions by primary users, which results in high packet drops and delays. In this letter, with the purpose of decreasing the number of switches made between channels, we propose a nonparametric channel allocation algorithm that uses robust kernel density estimation to effectively schedule idle channel resources. Experiment and simulation results demonstrate that the proposed algorithm outperforms both random and parametric channel allocation algorithms in terms of throughput and packet drops.

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References

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Cited by

  1. Sensing-Transmission Edifice Using Bayesian Nonparametric Traffic Clustering in Cognitive Radio Networks vol.13, pp.9, 2012, https://doi.org/10.1109/tmc.2013.156