Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning
This work addresses physical layer security for wireless networks, offering a more efficient solution for secure communication in challenging environments, though it appears incremental as it builds on existing IRS and deep learning techniques.
The paper tackled secure wireless communication in programmable environments using an intelligent reflecting surface (IRS) to maximize secrecy rates, achieving comparable performance to conventional methods while significantly reducing computational complexity.
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.