Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

The Study of Cognitive Radio Prediction Based on Big Data

Authors
Haoxiang Sun, Changxing Chen, Yunfei Ling, Jiyao Huang, Xiangyang Lin
Corresponding Author
Haoxiang Sun
Available Online March 2018.
DOI
10.2991/mecae-18.2018.70How to use a DOI?
Keywords
Big data, Prediction, Spectrum resources, Cognitive radio, Application advantages
Abstract

With the advent of the age of big dat, the application of big data to predict is a powerful way to solve various problems. However, for the increasingly tense spectrum resources, the prediction of cognitive radio based on big data is an inevitable trend. In this paper, the advantages of big data prediction technology in each process of cognitive radio are discussed, and the technical process and common prediction algorithms of cognitive radio based on big data are briefly described.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Series
Advances in Engineering Research
Publication Date
March 2018
ISBN
10.2991/mecae-18.2018.70
ISSN
2352-5401
DOI
10.2991/mecae-18.2018.70How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Haoxiang Sun
AU  - Changxing Chen
AU  - Yunfei Ling
AU  - Jiyao Huang
AU  - Xiangyang Lin
PY  - 2018/03
DA  - 2018/03
TI  - The Study of Cognitive Radio Prediction Based on Big Data
BT  - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.70
DO  - 10.2991/mecae-18.2018.70
ID  - Sun2018/03
ER  -