Emerging Technologies for Developing Countries. First International EAI Conference, AFRICATEK 2017, Marrakech, Morocco, March 27-28, 2017 Proceedings

Research Article

Analysis of the Impact of Cognitive Vehicular Network Environment on Spectrum Sensing

  • @INPROCEEDINGS{10.1007/978-3-319-67837-5_3,
        author={Amina Riyahi and Marouane Sebgui and Slimane Bah and Belhaj Elgraini},
        title={Analysis of the Impact of Cognitive Vehicular Network Environment on Spectrum Sensing},
        proceedings={Emerging Technologies for Developing Countries. First International EAI Conference, AFRICATEK 2017, Marrakech, Morocco, March 27-28, 2017 Proceedings},
        proceedings_a={AFRICATEK},
        year={2017},
        month={10},
        keywords={Cognitive radio CVNs Spectrum sensing Data fusion},
        doi={10.1007/978-3-319-67837-5_3}
    }
    
  • Amina Riyahi
    Marouane Sebgui
    Slimane Bah
    Belhaj Elgraini
    Year: 2017
    Analysis of the Impact of Cognitive Vehicular Network Environment on Spectrum Sensing
    AFRICATEK
    Springer
    DOI: 10.1007/978-3-319-67837-5_3
Amina Riyahi1,*, Marouane Sebgui1,*, Slimane Bah1,*, Belhaj Elgraini1,*
  • 1: University Mohammed V in Rabat
*Contact email: aminariyahi@research.emi.ac.ma, sebgui@emi.ac.ma, bah@emi.ac.ma, elgraini@emi.ac.ma

Abstract

The Cognitive Vehicular Network (CVN) has emerged as a promising solution providing additional resources and allowing spectrum efficiency. However, vehicular networks are highly challenging for spectrum sensing due to speed, mobility and dynamic topology. Furthermore, these parameters depend on the CVNs’ environment such as highway, urban or suburban. Therefore, solutions targeting CVNs should take into consideration these characteristics. As a first step towards an appropriate spectrum sensing solution for CVNs, we first, provide a comprehensive classification of existing spectrum sensing techniques for CVNs. Second, we discuss, for each class, the impact of the vehicular environment effects such as traffic density, speed and fading on the spectrum sensing and data fusion techniques. Finally we derive a set of requirements for CVN’s spectrum sensing that takes into consideration specific characteristics of CVN environments.