@inproceedings{02535708d26f44a0bae7c3a056998894,
title = "A relevance vector machine based probability prediction method of channel available time",
abstract = "The uncertainty of spectrum resources will seriously affect the prediction results of cognitive radio, and then affect the communication channel allocation and spectrum access. Therefore, it is very important to judge, analyze and estimate the state change of the spectrum resources. This paper introduces the RVM (Relevance Vector Machine) theory and put forward the probability interval prediction method of channel state duration. Based on the traditional machine learning, RVM is integrated with the Bayesian inference framework, and it can give the estimate value of the prediction error and give the prediction interval, which can cover the real value well.",
author = "Zhenyu Xu and Dezhi Li and Shuo Shi and Zhenbang Wang and Jiang, \{Jin Yao\}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 ; Conference date: 14-07-2017 Through 16-07-2017",
year = "2019",
doi = "10.1007/978-981-10-6571-2\_200",
language = "英语",
isbn = "9789811065705",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "1650--1658",
editor = "Qilian Liang and Min Jia and Jiasong Mu and Wei Wang and Xuhong Feng and Baoju Zhang",
booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems",
address = "德国",
}