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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2020
- DOI:
- 10.48550/arXiv.2004.03056
- arXiv:
- arXiv:2004.03056
- Bibcode:
- 2020arXiv200403056S
- Keywords:
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- Electrical Engineering and Systems Science - Signal Processing;
- Computer Science - Machine Learning
- E-Print:
- Submitted to IEEE