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

## TL;DR

This paper proposes a new method using intelligent reflecting surfaces and deep reinforcement learning to improve wireless communication security against eavesdroppers and jamming attacks.

## Contribution

The novel Deep-PLS approach optimizes beamforming for enhanced physical layer security in dynamic environments.

## Key findings

- Deep-PLS outperforms traditional IRS methods in secrecy performance.
- The proposed strategy effectively redirects jamming signals to improve signal reception for legitimate users.
- Simulation results show superior performance compared to benchmark strategies.

## Abstract

This article introduces a novel strategy for wireless communication security utilizing intelligent reflecting surfaces (IRS). The IRS is strategically deployed to mitigate jamming attacks and eavesdropper threats while improving signal reception for legitimate users (LUs) by redirecting jamming signals toward desired communication signals leveraging physical layer security (PLS). By integrating the IRS into the backscatter communication system, we enhance the overall secrecy rate of LU, by dynamically adjusting IRS reflection coefficients and active beamforming at the base station (BS). A design problem is formulated to jointly optimize IRS reflecting beamforming and BS active beamforming, considering time-varying channel conditions and desired secrecy rate requirements. We propose a novel approach based on deep reinforcement learning (DRL) named Deep-PLS. This approach aims to determine an optimal beamforming policy capable of thwarting eavesdroppers in evolving environmental conditions. Extensive simulation studies validate the efficacy of our proposed strategy, demonstrating superior performance compared to traditional IRS approaches, IRS backscattering-based anti-eavesdropping methods, and other benchmark strategies in terms of secrecy performance.