RFML-Driven Spectrum Prediction: A Novel Model-Enabled Autoregressive Network
Author:
Affiliation:
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2. Department of Electrical and Computer Engineering, Utah State University, Logan, UT, USA
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/9881201/09828536.pdf?arnumber=9828536
Reference7 articles.
1. Prediction and Modeling of Spectrum Occupancy for Dynamic Spectrum Access Systems
2. The RFML ecosystem: A look at the unique challenges of applying deep learning to radio frequency applications;wong;arXiv 2010 00432,2020
3. Spectrum Prediction Based on Taguchi Method in Deep Learning With Long Short-Term Memory
4. Experimental Results on the Impact of Memory in Neural Networks for Spectrum Prediction in Land Mobile Radio Bands
5. Sequential Likelihood Ratio Test under Incomplete Signal Model for Spectrum Sensing
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