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Cooperative Jamming and Relay Selection for Covert Communications Based on Reinforcement Learning
Jin Qian1, Hui Li1, Pengcheng Zhu1
1College of Information Engineering, Taizhou University, Taizhou 225300, China.
Sensors (Basel, Switzerland)
|October 16, 2025
Summary
This study introduces a reinforcement learning framework to enhance covert wireless communications. It optimizes relay selection and jamming strategies, outperforming random methods for secure data transmission.
Area of Science:
- Wireless Communication Security
- Machine Learning Applications
- Network Optimization
Background:
- Maintaining covert transmissions in wireless networks is challenging due to collaborative wardens.
- Existing methods struggle with dynamic network conditions and adversarial threats.
Purpose of the Study:
- To develop a reinforcement learning framework for optimizing cooperative jamming and relay selection in covert wireless networks.
- To enhance covert throughput and ensure warden detection failure probability under power constraints.
Main Methods:
- A reinforcement learning-based relay selection scheme (RLRS) was developed.
- The framework jointly optimizes relay forwarding and cooperative jamming strategies.
- The approach was evaluated in a multi-relay-assisted two-hop network.
Main Results:
- The RLRS significantly outperformed random relay selection (RRS).
- Achieved higher peak covert transmission rates and lower outage probabilities.
- Demonstrated superior adaptability to varying network parameters like relay density and noise.
Conclusions:
- Reinforcement learning is effective for optimizing secure covert communications.
- The proposed framework enhances network security against colluding wardens.
- The study validates the use of RL for dynamic relay and jammer selection.
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