Defeating Proactive Jammers Using Deep Reinforcement Learning for Resource-Constrained IoT Networks
Author:
Affiliation:
1. Khalifa University,Department of Electrical Engineering and Computer Science,Abu Dhabi,UAE,127788
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10293227/10293746/10293793.pdf?arnumber=10293793
Reference19 articles.
1. Deep Reinforcement Learning Based Multi-User Anti-Jamming Strategy
2. Two-Dimensional Antijamming Mobile Communication Based on Reinforcement Learning
3. RF jamming dataset: A wireless spectral scan approach for malicious interference detection;ali;TechRxiv Preprint,2022
4. Deep Reinforcement Learning Based Anti-Jamming Using Clear Channel Assessment Information in a Cognitive Radio Environment
5. Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks
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1. Reinforcement Learning for Resilient Aerial-IRS Assisted Wireless Communications Networks in the Presence of Multiple Jammers;IEEE Open Journal of the Communications Society;2024
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