Deep Stacked Autoencoder-Based Long-Term Spectrum Prediction Using Real-World Data-Reference-Cited by-同舟云学术

Deep Stacked Autoencoder-Based Long-Term Spectrum Prediction Using Real-World Data

Published:2023-06 Issue:3 Volume:9 Page:534-548
ISSN:2332-7731
Container-title:IEEE Transactions on Cognitive Communications and Networking
language:
Short-container-title:IEEE Trans. Cogn. Commun. Netw.

Author:

Pan Guangliang¹^ORCID,Wu Qihui¹^ORCID,Ding Guoru²^ORCID,Wang Wei¹^ORCID,Li Jie¹^ORCID,Xu Fuyuan³,Zhou Bo¹^ORCID

Affiliation:

1. Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. College of Communications Engineering, Army Engineering University, Nanjing, China

3. Nanjing Electronic Equipment Research Institute, Nanjing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/6687307/10146027/10064355.pdf?arnumber=10064355

Reference41 articles.

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2. Electrosense: Open and Big Spectrum Data

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4. Correlation Coefficients

5. Experimental Results on the Impact of Memory in Neural Networks for Spectrum Prediction in Land Mobile Radio Bands

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