Deep Reinforcement Learning for Anti-Jamming Dynamic Spectrum Access: A Bootstrap Ensemble Approach with Echo State Network and Idle-Ratio Change Detection

Hao Jiang, Xin Bian, Mingqi Li

Open source

DOI
10.3390/s26154737
Published
2026-07-26
Container
Sensors
Publisher
MDPI AG
Open access
unknown

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BibTeX

@article{allodium:10.3390/s26154737,
  title = {Deep Reinforcement Learning for Anti-Jamming Dynamic Spectrum Access: A Bootstrap Ensemble Approach with Echo State Network and Idle-Ratio Change Detection},
  author = {Hao Jiang and Xin Bian and Mingqi Li},
  year = {2026},
  journal = {Sensors},
  doi = {10.3390/s26154737},
  url = {https://doi.org/10.3390/s26154737}
}

RIS

TY  - JOUR
TI  - Deep Reinforcement Learning for Anti-Jamming Dynamic Spectrum Access: A Bootstrap Ensemble Approach with Echo State Network and Idle-Ratio Change Detection
AU  - Hao Jiang
AU  - Xin Bian
AU  - Mingqi Li
PY  - 2026
JO  - Sensors
DO  - 10.3390/s26154737
UR  - https://doi.org/10.3390/s26154737
ER  - 

APA

Jiang, H., Bian, X., & Li, M. (2026). Deep Reinforcement Learning for Anti-Jamming Dynamic Spectrum Access: A Bootstrap Ensemble Approach with Echo State Network and Idle-Ratio Change Detection. Sensors. https://doi.org/10.3390/s26154737

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