计算机工程与应用2024，Vol.60Issue(5)：299-306,8.DOI:10.3778/j.issn.1002-8331.2210-0295
融合DAE-LSTM的认知物联网智能频谱感知算法
DAE-LSTM-Fused Intelligent Spectrum Sensing Algorithm for Cognitive Internet of Things
摘要
Abstract
The rise of the fifth-generation(5G)mobile communication,the development of Internet of things(IoT)is pro-moted.However,with the explosive growth of IoT data transmission volume,the shortage of spectrum resources is becoming more and more severe.Spectrum sensing technology greatly improves the spectrum utilization of the Internet of things.However,the IoT mobile communication environment has the characteristics of high complexity and easy signal distor-tion,which poses a major challenge to the existing spectrum sensing.Thus,this paper proposes an intelligent spectrum sensing algorithm that fused with denoising autoencoder(DAE)and improved long short term memory(LSTM)neural network.DAE excavates the internal structural features of mobile signals through encoding and decoding.The improved LSTM spectrum sensing classifier model is designed to classify time series signal sequences combined with past moment information features.Finally,the proposed algorithm achieves 45% higher sensing accuracy than support vector machine(SVM),Elman,LeNet5,learning vector quantization(LVQ)and recurrent neural network(RNN)algorithms.关键词
认知物联网/智能频谱感知/去噪自编码器/长短时记忆网络Key words
cognitive Internet of things(IoT)networks/intelligent spectrum sensing/denoising autoencoder(DAE)/long short term memory(LSTM)分类
信息技术与安全科学引用本文复制引用
段闫闫,徐凌伟..融合DAE-LSTM的认知物联网智能频谱感知算法[J].计算机工程与应用,2024,60(5):299-306,8.基金项目
国家自然科学基金(62201313) （62201313）
数字化学习技术集成与应用教育部工程研究中心创新基金(1221042) （1221042）
汽车新技术安徽省工程技术研究中心开放基金(QCKJ202101). （QCKJ202101）
