Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning

Abstract

This paper considers machine learning for physical layer security design for communication in a challenging wireless environment. The radio environment is assumed to be programmable with the aid of a meta material-based intelligent reflecting surface (IRS) allowing customisable path loss, multi-path fading and interference effects. In particular, the fine-grained reflections from the IRS elements are exploited to create channel advantage for maximizing the secrecy rate at a legitimate receiver. A deep learning (DL) technique has been developed to tune the reflections of the IRS elements in real-time. Simulation results demonstrate that the DL approach yields comparable performance to the conventional approaches while significantly reducing the computational complexity.

arXiv commentSubmitted to IEEE

References in corpus (4)

• Secure Wireless Communication via Intelligent Reflecting Surface
• Joint Beamforming Design in Multi-Cluster MISO NOMA Intelligent Reflecting Surface-Aided Downlink Communication Networks
• Enabling Secure Wireless Communications via Intelligent Reflecting Surfaces
• Learning the Wireless V2I Channels Using Deep Neural Networks

Cited by in corpus (6)

• Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach
• Enhancing Secure MIMO Transmission via Intelligent Reflecting Surface
• Joint Beamforming and Location Optimization for Secure Data Collection in Wireless Sensor Networks with UAV-Carried Intelligent Reflecting Surface
• Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications
• Designing IRS-Aided MIMO Systems for Secrecy Enhancement