Partitioned RIS-Assisted Vehicular Secure Communication Based on Meta-Learning and Reinforcement Learning.

Area of Science:

• Wireless Communications
• Network Security
• Artificial Intelligence

Background:

• Vehicular ad hoc networks (VANETs) face dynamic eavesdropping threats.
• Adaptive eavesdroppers challenge secure communication in VANETs.
• Reconfigurable intelligent surfaces (RIS) offer potential for signal manipulation.

Purpose of the Study:

• To develop a secure communication scheme for VANETs against dynamic eavesdropping.
• To optimize the use of a partitioned RIS for enhanced signal and artificial noise (AN) transmission.
• To integrate meta-learning and reinforcement learning (RL) for adaptive security optimization.

Main Methods:

• A partitioned RIS is employed to simultaneously enhance legitimate signals and direct AN towards eavesdroppers.
• Meta-learning is utilized for rapid RIS partitioning adaptation to new eavesdropping scenarios.
• Reinforcement learning (RL) optimizes beamforming vectors and RIS reflection coefficients for improved security.
• A joint optimization framework integrates meta-learning and RL for dynamic performance enhancement.

Main Results:

• The proposed scheme achieves a 28% higher secrecy rate compared to conventional RIS-assisted methods.
• The framework demonstrates faster convergence than traditional deep learning approaches.
• The system effectively balances signal enhancement with jamming interference for robust security.
• Simulations confirm the approach's effectiveness in dynamic vehicular environments.

Conclusions:

• The integrated meta-learning and RL framework provides robust and energy-efficient security for VANETs.
• The partitioned RIS strategy effectively mitigates dynamic eavesdropping threats.
• The adaptive optimization approach ensures high secrecy rates in rapidly changing network conditions.