Online Spectrum Sensing in Cognitive Radio

DOI

10.2991/cnct-16.2017.22How to use a DOI?

Keywords

PUs, Hidden Markov model, Particle filter, Bayesian filter, Mixed filter

Abstract

Spectrum sensing is an essential part for cognitive radio which is used for directly or indirectly estimating the states of primary users (PUs) to find spectrum holes for second users (SUs). Nowadays, some online spectrum sensing algorithms have been proposed but most of them ignore the time complexity which influences the efficiency of spectrum sensing. In this paper we propose a new filter algorithm named Mixed filter to address this problem. Leveraged by the Hidden Markov model, this algorithm provides an alternative way to estimate parameters of the model online. Extensive simulations have been conducted and the simulation results show that the proposed method can reduce the computational complexity and shorten the executing time withoug out reducing the accuracy.

Copyright

© 2017, 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  - Wen-xiao WANG
AU  - Xue-mei BAI
AU  - Zhi-jun WANG
AU  - Bin GUO
PY  - 2016/12
DA  - 2016/12
TI  - Online Spectrum Sensing in Cognitive Radio
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 164
EP  - 170
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.22
DO  - 10.2991/cnct-16.2017.22
ID  - WANG2016/12
ER  -