Spectrum Sensing Algorithm Based on Fisher Classification

DOI

10.2991/cecs-18.2018.29How to use a DOI?

Keywords

Pattern Recognition, Fisher Classification, Spectrum Sensing, Historial Data

Abstract

This paper presents a novel spectrum sensing algorithm based on Fisher classification for spectrum sensing speed, accuracy and reliability. With application of the pattern recognition theory to the process of specetrum sensing modelling, the spectral holes are employed to the greatest possible advantage and the sensing performaces are improved at low signal-noise-ratio (SNR). The signal power and SNR characterize the electromagnetic environment from the data for training. The simulation result shows that the novel algorithm has a better performance under the low SNR conditions, and the detection probability of spectrum sensing can go up in propotion to the number of the historical data.

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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TY  - CONF
AU  - Wenxiang Guo
AU  - Zhiyong Yu
AU  - Rui Jin
PY  - 2018/07
DA  - 2018/07
TI  - Spectrum Sensing Algorithm Based on Fisher Classification
BT  - Proceedings of the 2018 International Symposium on Communication Engineering & Computer Science (CECS 2018)
PB  - Atlantis Press
SP  - 153
EP  - 158
SN  - 2352-538X
UR  - https://doi.org/10.2991/cecs-18.2018.29
DO  - 10.2991/cecs-18.2018.29
ID  - Guo2018/07
ER  -