Rethinking Autocorrelation for Deep Spectrum Sensing in Cognitive Radio Networks

Author:

Chae Keunhong1ORCID,Park Jungin2,Kim Yusung3ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea

2. Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea

3. College of Computing and Informatics, Sungkyunkwan University, Suwon, Republic of Korea

Funder

Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant

Korea Government (MSIT) through the Development of Incumbent Radio Stations Protection and Frequency Sharing Technology through Spectrum Challenge

Development of 5G Edge Security Technology for Ensuring 5G+ Service Stability and Availability

Self-Directed Multi-Modal Intelligence for Solving Unknown, Open Domain Problems

Artificial Intelligence Graduate School Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Spectrum Sensing Method Based on STFT-RADN in Cognitive Radio Networks;Sensors;2024-09-06

2. Wideband Power Spectrum Sensing: A Fast Practical Solution for Nyquist Folding Receiver;IEEE Internet of Things Journal;2024-07-01

3. CNN-LSTM based Spectrum Sensing in Full-Duplex-aided CR-IoT Networks;Proceedings of the Cognitive Models and Artificial Intelligence Conference;2024-05-25

4. Sub-band Selection for Transform Domain Communication System Based on DRQN;2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI);2024-05-24

5. A Fast Power Spectrum Sensing Solution for Generalized Coprime Sampling;Remote Sensing;2024-02-26

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