Intelligent reflecting surface backscatter-enabled physical layer security enhancement via deep reinforcement...

Area of Science:

• Wireless Communication Security
• Intelligent Reflecting Surfaces (IRS)
• Physical Layer Security (PLS)

Background:

• Jamming attacks and eavesdropper threats compromise wireless communication security.
• Intelligent Reflecting Surfaces (IRS) offer a promising solution for mitigating these threats.
• Integrating IRS into backscatter communication systems can enhance signal reception and secrecy rates for legitimate users (LUs).

Purpose of the Study:

• To introduce a novel strategy for wireless communication security using IRS.
• To enhance the secrecy rate of legitimate users (LUs) by mitigating jamming and eavesdropper threats.
• To develop an optimal beamforming policy for thwarting eavesdroppers in dynamic environments.

Main Methods:

• Strategic deployment of IRS to redirect jamming signals and protect desired communication signals.
• Joint optimization of IRS reflection coefficients and base station (BS) active beamforming.
• Development of a deep reinforcement learning (DRL) approach named Deep-PLS to determine optimal beamforming policies.

Main Results:

• The proposed Deep-PLS strategy effectively mitigates jamming attacks and eavesdropper threats.
• Dynamic adjustment of IRS reflection coefficients and BS active beamforming significantly enhances the secrecy rate of LUs.
• The strategy demonstrates superior performance compared to traditional IRS methods and other benchmark strategies.

Conclusions:

• The novel IRS-based strategy, optimized via Deep-PLS, provides a robust solution for wireless communication security.
• The approach effectively improves secrecy performance by intelligently managing signal reflection and beamforming.
• Deep-PLS offers a powerful tool for securing wireless communications against evolving threats.