通信学报2023，Vol.44Issue(12)：99-111,13.DOI:10.11959/j.issn.1000-436x.2023234
基于模型数据双驱动的短波MUF短期预测网络
Short-term prediction network for short-wave MUF based on model-data dual-driven
摘要
Abstract
Predicting the maximum available frequency of short-wave communication presents the challenges of low prediction accuracy of classical prediction model methods and difficulty in obtaining training set data for machine learn-ing prediction methods.To address this issue,a model-data dual-driven bidirectional gated recurrent unit(BiGRU)net-work for short-term prediction of MUF was proposed.On the model-driven,a large-scale dataset generated by the classi-cal MUF prediction model was used as the model-driven training set,and a preliminary network was obtained after joint learning of the 2D CNN and the BiGRU network.On the data-driven,the preliminary network was trained twice using a small-scale measured dataset to obtain the final network CNN-BiGRU-NN.The simulation results show that the proposed network has reduced average root mean squared error(RMSE)at both daily and momentary scales compared with the GRU network,LSTM network and VOACAP model.关键词
短波通信/最大可用频率/短期预测/模型数据双驱动/CNN-BiGRU-NNKey words
short-wave communication/maximum usable frequency/short-term prediction/model-data dual-driven/CNN-BiGRU-NN分类
信息技术与安全科学引用本文复制引用
李俊兵,曾囿钧,曾孝平,李国军,白晨曦..基于模型数据双驱动的短波MUF短期预测网络[J].通信学报,2023,44(12):99-111,13.基金项目
国家自然科学基金资助项目(No.U21A20448,No.U20A20157,No.U22A2006) （No.U21A20448,No.U20A20157,No.U22A2006）
重庆市基础研究与前沿探索基金资助项目(No.cstc2021ycjh-bgzxm0072)The National Natural Science Foundation of China(No.U21A20448,No.U20A20157,No.U22A2006),The Chongqing Basic Research and Frontier Exploration Project(No.cstc2021ycjh-bgzxm0072) （No.cstc2021ycjh-bgzxm0072）
