ISCAS OpenIR
"Soft decision" spectrum prediction based on back-propagation neural networks
Bai, Suya (1); Zhou, Xin (1); Xu, Fanjiang (1)
2014
Conference Name2014 IEEE International Conference on Computing, Management and Telecommunications, ComManTel 2014
Pages128-133
Conference DateApril 27, 2014 - April 29, 2014
Conference PlaceDa Nang, Viet nam
Indexed TypeEI
Publish PlaceIEEE Computer Society
ISBN9781479929030
Department(1) Science and Technology on Integrated Information System Laboratory, Institute of Software Chinese Academy of Sciences, Beijing, China
English AbstractIn the cognitive radio system, spectrum prediction attracts more and more attention, which predicts future spectrum holes to save energy of spectrum sensing and to improve the efficiency of spectrum access. The current research on spectrum prediction is similar to the hard decision in the communication system. However, the hard decision loses amount of channel information during the process of obtaining channel statuses, which decreases the predictive accuracy of spectrum prediction. Therefore, we propose a "soft decision" model for spectrum prediction based on back-propagation neural networks. In the proposed model, the power values of frequency sampling point instead of the channel status are used as the inputs of the spectrum prediction model. Our experimental results demonstrate that the predictive accuracy of the proposed "soft decision" spectrum prediction model is better than the performance of conventional "hard decision". © 2014 IEEE.; In the cognitive radio system, spectrum prediction attracts more and more attention, which predicts future spectrum holes to save energy of spectrum sensing and to improve the efficiency of spectrum access. The current research on spectrum prediction is similar to the hard decision in the communication system. However, the hard decision loses amount of channel information during the process of obtaining channel statuses, which decreases the predictive accuracy of spectrum prediction. Therefore, we propose a "soft decision" model for spectrum prediction based on back-propagation neural networks. In the proposed model, the power values of frequency sampling point instead of the channel status are used as the inputs of the spectrum prediction model. Our experimental results demonstrate that the predictive accuracy of the proposed "soft decision" spectrum prediction model is better than the performance of conventional "hard decision". © 2014 IEEE.
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/16638
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Bai, Suya ,Zhou, Xin ,Xu, Fanjiang . "Soft decision" spectrum prediction based on back-propagation neural networks[C]. IEEE Computer Society,2014:128-133.
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