LSTM Based Spectrum Prediction for Real-Time Spectrum Access for IoT Applications

作     者：R.Nandakumar Vijayakumar Ponnusamy Aman Kumar Mishra 

作者机构：KSR Institute for Engineering and TechnologyTiruchencode637215India ECE DepartmentSRMISTChengalpattuChennai603202India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第3期

页      面：2805-2819页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：Cognitive radio network encoder-decoder LSTM waiting time capacity 

摘      要：In the Internet of Things(IoT)scenario,many devices will communi-cate in the presence of the cellular network;the chances of availability of spec-trum will be very scary given the presence of large numbers of mobile users and large amounts of *** prediction is very encouraging for high traffic next-generation wireless networks,where devices/machines which are part of the Cognitive Radio Network(CRN)can predict the spectrum state prior to transmission to save their limited energy by avoiding unnecessarily sen-sing radio *** short-term memory(LSTM)is employed to simulta-neously predict the Radio Spectrum State(RSS)for two-time slots,thereby allowing the secondary node to use the prediction result to transmit its information to achieve lower waiting time hence,enhanced performance capacity.A frame-work of spectral transmission based on the LSTM prediction is formulated,named as positive prediction and sensing-based spectrum *** proposed scheme provides an average maximum waiting time gain of 2.88 *** proposed scheme provides 0.096 bps more capacity than a conventional energy detector.