Quality, Reliability, Security and Robustness in Heterogeneous Networks. 7th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2010, and Dedicated Short Range Communications Workshop, DSRC 2010, Houston, TX, USA, November 17-19, 2010, Revised Selected Papers

Research Article

Spectrum Prediction via Temporal Conditional Gaussian Random Field Model in Wideband Cognitive Radio Networks

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  • @INPROCEEDINGS{10.1007/978-3-642-29222-4_2,
        author={Zhenghao Zhang and Husheng Li and Hannan Ma and Kun Zheng and Depeng Yang and Changxing Pei},
        title={Spectrum Prediction via Temporal Conditional Gaussian Random Field Model in Wideband Cognitive Radio Networks},
        proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Networks. 7th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2010, and Dedicated Short Range Communications Workshop, DSRC 2010, Houston, TX, USA, November 17-19, 2010, Revised Selected Papers},
        proceedings_a={QSHINE},
        year={2012},
        month={10},
        keywords={cognitive radio networks net components dynamic software architecture modeling agents software development approach},
        doi={10.1007/978-3-642-29222-4_2}
    }
    
  • Zhenghao Zhang
    Husheng Li
    Hannan Ma
    Kun Zheng
    Depeng Yang
    Changxing Pei
    Year: 2012
    Spectrum Prediction via Temporal Conditional Gaussian Random Field Model in Wideband Cognitive Radio Networks
    QSHINE
    Springer
    DOI: 10.1007/978-3-642-29222-4_2
Zhenghao Zhang,*, Husheng Li1,*, Hannan Ma1, Kun Zheng1, Depeng Yang1, Changxing Pei2
  • 1: The University of Tennessee
  • 2: Xidian University
*Contact email: chancejack@gmail.com, husheng@eecs.utk.edu

Abstract

Wideband spectrum sensing remains an open challenge for cognitive radio networks due to the insufficient wideband sensing capability. This paper introduces the theory of Gaussian Markov Random Field to estimate the un-sensed sub-channel status. We set up a measurement system to capture the WiFi spectrum data. With the measurement data, we verify that the proposed model of Temporal Conditional Gaussian Random Field can efficient estimate the sub-channel status.