Mobile Lightweight Wireless Systems. First International ICST Conference, MOBILIGHT 2009, Athens, Greece, May 18-20, 2009, Revised Selected Papers

Research Article

Cooperative Spectrum Sensing for Cognitive Radios: Performance Analysis for Realistic System Setups and Channel Conditions

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  • @INPROCEEDINGS{10.1007/978-3-642-03819-8_13,
        author={Marco Renzo and Laura Imbriglio and Fabio Graziosi and Fortunato Santucci and Christos Verikoukis},
        title={Cooperative Spectrum Sensing for Cognitive Radios: Performance Analysis for Realistic System Setups and Channel Conditions},
        proceedings={Mobile Lightweight Wireless Systems. First International ICST Conference, MOBILIGHT 2009, Athens, Greece, May 18-20, 2009, Revised Selected Papers},
        proceedings_a={MOBILIGHT},
        year={2012},
        month={6},
        keywords={Cognitive Radio Spectrum Sensing Cooperative Communications Correlated Log--Normal Shadowing Performance Analysis},
        doi={10.1007/978-3-642-03819-8_13}
    }
    
  • Marco Renzo
    Laura Imbriglio
    Fabio Graziosi
    Fortunato Santucci
    Christos Verikoukis
    Year: 2012
    Cooperative Spectrum Sensing for Cognitive Radios: Performance Analysis for Realistic System Setups and Channel Conditions
    MOBILIGHT
    Springer
    DOI: 10.1007/978-3-642-03819-8_13
Marco Renzo1,*, Laura Imbriglio2,*, Fabio Graziosi2,*, Fortunato Santucci2,*, Christos Verikoukis1,*
  • 1: Telecommunications Technological Center of Catalonia (CTTC)
  • 2: University of L’Aquila
*Contact email: marco.di.renzo@cttc.es, laura.imbriglio@univaq.it, fabio.graziosi@univaq.it, fortunato.santucci@univaq.it, cveri@cttc.es

Abstract

In this paper, we propose an analytical framework for analysis and design of cooperative spectrum sensing methods over correlated Log–Normal shadow–fading environments, when each cooperative user makes use of a simple Amplify and Forward (AF) relaying mechanism to send the detected signal to a sink node. We will show that the framework requires efficient and accurate methods for modeling the power–sum of correlated Log–Normal Random Variables (RVs), which well describe shadowing phenomena, and propose novel approximation methods to efficiently solve this problem. Numerical results will be shown to substantiate the proposed framework.