Research Article
Research on Cooperative Spectrum Sensing Algorithm
@INPROCEEDINGS{10.1007/978-3-319-52730-7_35, author={Yu Gao and Xin-Lin Huang and Si-Yue Sun and Xiaowei Tang and Yuan Xu}, title={Research on Cooperative Spectrum Sensing Algorithm}, proceedings={Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers}, proceedings_a={MLICOM}, year={2017}, month={2}, keywords={Cognitive radio Cooperative spectrum sensing Undersampling Vector orthogonal frequency-division multiplexing}, doi={10.1007/978-3-319-52730-7_35} }- Yu Gao
Xin-Lin Huang
Si-Yue Sun
Xiaowei Tang
Yuan Xu
Year: 2017
Research on Cooperative Spectrum Sensing Algorithm
MLICOM
Springer
DOI: 10.1007/978-3-319-52730-7_35
Abstract
The rapid development of wireless communication brings us convenience as well as scarcity of radio spectrum resources. Hence, scientists proposed cognitive radio technology to solve this problem. Spectrum sensing is a pivotal technology protecting primary users from interference of secondary users in cognitive radio, and can be achieved by different algorithms which will result in different performances. In this paper an original cooperative broadband spectrum sensing algorithm based on undersampling is proposed to reduce the hardware overhead as well as satisfying the requirement of system performance. The proposed cooperative spectrum sensing algorithm will use undersampling technology in the secondary user in order to save costs and reduce hardware overhead. On this premise, in the process of information transmission, the algorithm have adopted a method which is similar to VOFDM for signal transmission in the channel between secondary users and fusion center, so that the system can overcome the intersymbol interference caused by broadband signal and rebuild the state of primary users in the fusion center. The simulation results shows that the performance of proposed algorithm is similar to the traditional single-node spectrum sensing algorithm and “or” decision algorithm, however, worse than “and” decision algorithm. The performance loss is acceptable considering its effect of reducing hardware overhead.