"Soft decision" spectrum prediction based on back-propagation neural networks

ISCAS OpenIR

	"Soft decision" spectrum prediction based on back-propagation neural networks
	Bai, Suya (1); Zhou, Xin (1); Xu, Fanjiang (1)
	2014
会议名称	2014 IEEE International Conference on Computing, Management and Telecommunications, ComManTel 2014
页码	128-133
会议日期	April 27, 2014 - April 29, 2014
会议地点	Da Nang, Viet nam
收录类别	EI
出版地	IEEE Computer Society
ISBN	9781479929030
部门归属	(1) Science and Technology on Integrated Information System Laboratory, Institute of Software Chinese Academy of Sciences, Beijing, China
摘要	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.; 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.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16638
专题	中国科学院软件研究所
推荐引用方式 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.