Counting Credibility based Cooperative Spectrum Sensing Algorithm

Lianlian Song, Li Wang, Shibing Zhang

Abstract

In the cooperative spectrum sensing, if too many nodes take part in the cooperative data fusion, it would weigh the channel overhead and energy loss lot but improve the spectrum sensing performance little. This paper focuses on the channel overhead of cooperative spectrum sensing and the lifecycle of cognitive networks, and proposes a novel cooperative spectrum sensing algorithm. In the algorithm, all of the nodes are sorted by means of counting reliability. Only a part of nodes participate in the cooperative data fusion in the fusion centre. It cut down the number of nodes participating in the data fusion and save the average energy of the sensing nodes. The simulation results show that the proposed algorithm can effectively reduce channel overhead and prolong the lifecycle of cognitive network in the premise of ensuring the spectrum detection performance.

References

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Paper Citation


in Harvard Style

Song L., Wang L. and Zhang S. (2015). Counting Credibility based Cooperative Spectrum Sensing Algorithm . In Proceedings of the 12th International Conference on Wireless Information Networks and Systems - Volume 1: WINSYS, (ICETE 2015) ISBN 978-989-758-119-9, pages 71-75. DOI: 10.5220/0005574700710075


in Bibtex Style

@conference{winsys15,
author={Lianlian Song and Li Wang and Shibing Zhang},
title={Counting Credibility based Cooperative Spectrum Sensing Algorithm},
booktitle={Proceedings of the 12th International Conference on Wireless Information Networks and Systems - Volume 1: WINSYS, (ICETE 2015)},
year={2015},
pages={71-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005574700710075},
isbn={978-989-758-119-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Wireless Information Networks and Systems - Volume 1: WINSYS, (ICETE 2015)
TI - Counting Credibility based Cooperative Spectrum Sensing Algorithm
SN - 978-989-758-119-9
AU - Song L.
AU - Wang L.
AU - Zhang S.
PY - 2015
SP - 71
EP - 75
DO - 10.5220/0005574700710075