7th International Conference on Cognitive Radio Oriented Wireless Networks

Research Article

Bayesian Approach to Spectrum Sensing for Cognitive Radio Applications

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  • @INPROCEEDINGS{10.4108/icst.crowncom.2012.248512,
        author={Ahmet Gokceoglu and Robert Piche and Mikko Valkama},
        title={Bayesian Approach to Spectrum Sensing for Cognitive Radio Applications},
        proceedings={7th International Conference on Cognitive Radio Oriented Wireless Networks},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2012},
        month={7},
        keywords={spectrum sensing bayesian detection energy detection opportunistic spectrum access},
        doi={10.4108/icst.crowncom.2012.248512}
    }
    
  • Ahmet Gokceoglu
    Robert Piche
    Mikko Valkama
    Year: 2012
    Bayesian Approach to Spectrum Sensing for Cognitive Radio Applications
    CROWNCOM
    IEEE
    DOI: 10.4108/icst.crowncom.2012.248512
Ahmet Gokceoglu1, Robert Piche1, Mikko Valkama1,*
  • 1: Tampere University of Technology
*Contact email: mikko.e.valkama@tut.fi

Abstract

In this paper, we address the spectrum sensing task of cognitive radio from Bayesian detection (BD) perspective. We first show that BD essentially simplifies to classical energy detection (ED) under Gaussian signal assumption but the threshold setting has more degrees of freedom to optimize the sensing performance, e.g., against given spectrum utilization. Then we propose a novel BD based algorithm where the sample energy is calculated iteratively, and the odds ratio is used to quantify the measurement reliability. Depending on the reliability, either a hard decision is forced or the algorithm progresses to accumulate more sample energy. When working under unknown SNRs, this allows the detector to reach reliable sensing decisions by using adaptive sample window, thus providing advantage over classical ED where fixed threshold is used regardless of channel conditions. Extensive computer simulations are provided to illustrate the performance advantages against classical ED in terms of e.g. sensing time.