Spectrum sensing via event-triggered sampling

Yasin Yilmaz, George Moustakides, Xiaodong Wang

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

7 Scopus citations


We propose a new framework for cooperative spectrum sensing in cognitive radio networks, that is based on a novel class of non-uniform samplers called the event-triggered samplers, and sequential detection. In the proposed scheme, each secondary user computes its local sensing decision statistic based on its own channel output; and whenever such decision statistic crosses certain predefined threshold values, the secondary user sends one bit (or several bits) information to the fusion center. The fusion center asynchronously receives the bits from different secondary users, based on which it updates the global sensing decision statistic and performs the sequential probability ratio test (SPRT), until reaching a sensing decision. We provide an asymptotic analysis for the above scheme, and under different conditions, we compare it against the cooperative sensing scheme that is based on traditional uniform sampling and sequential detection. Through simulations we show show that the proposed scheme, using even 1 bit, can outperform its uniform sampling counterpart that uses infinite number of bits.

Original languageEnglish (US)
Title of host publicationConference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Number of pages5
StatePublished - 2011
Externally publishedYes
Event45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States
Duration: Nov 6 2011Nov 9 2011

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Country/TerritoryUnited States
CityPacific Grove, CA

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications


  • asymptotic optimality
  • decentralized detection
  • event-triggered sampling
  • sequential sensing


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