Stochastic multichannel sensing for cognitive radio systems: Optimal channel selection for sensing with interference constraints

Gosan Noh, Jemin Lee, Daesik Hong

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

10 Citations (Scopus)

Abstract

This paper considers the problem of sensing and transmission strategy of multiple parallel channels owned by the primary user, referred as stochastic multichannel sensing. The traffic parameters follow the Markovian traffic assumption and are not identically distributed among the channels. In order to obtain the optimal probabilities of channel selection for sensing, we formulate a maximization problem for the secondary user throughput with interference constraints to the primary user. The solution to the problem is obtained via linear programming. Numerical results show that the proposed stochastic sensing achieves higher normalized effective throughput and lower average collision probability than the conventional deterministic sensing in a non-identical traffic environment. Additionally, the proposed method greatly reduces computational overheads and memory space.

Original languageEnglish
Title of host publicationProceedings of the 2009 IEEE 70th Vehicular Technology Conference Fall, VTC 2009 Fall
DOIs
Publication statusPublished - 2009
Event2009 IEEE 70th Vehicular Technology Conference Fall, VTC 2009 Fall - Anchorage, AK, United States
Duration: 2009 Sep 202009 Sep 23

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252

Other

Other2009 IEEE 70th Vehicular Technology Conference Fall, VTC 2009 Fall
CountryUnited States
CityAnchorage, AK
Period09/9/2009/9/23

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Stochastic multichannel sensing for cognitive radio systems: Optimal channel selection for sensing with interference constraints'. Together they form a unique fingerprint.

Cite this