Cross-Band Spectrum Prediction Based on Deep Transfer Learning
Cross-Band Spectrum Prediction Based on Deep Transfer Learning作者机构：College of Communications Engineering Army Engineering University
出 版 物：《China Communications》 (中国通信（英文版）)
年 卷 期：2020年第17卷第2期
页 面：66-80页
核心收录：
学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081104[工学-模式识别与智能系统] 0839[工学-网络空间安全] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Key R&D Program of China under Grant 2018AAA0102303 and Grant 2018YFB1801103 the National Natural Science Foundation of China (No. 61871398 and No. 61931011) the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20190030) the Equipment Advanced Research Field Foundation (No. 61403120304)
主 题：cross-band spectrum prediction deep transfer learning long short-term memory dynamic time warping transfer component analysis
摘 要：Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.
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