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

Zhenghao Zhang; Husheng Li; Hannan Ma; Kun Zheng; Depeng Yang; Changxing Pei

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

Download

525 downloads

Cite: BibTeX Plain Text

@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.

Keywords: cognitive radio networks net components dynamic software architecture modeling agents software development approach

Published: 2012-10-16

: http://dx.doi.org/10.1007/978-3-642-29222-4_2