Song, Y;
Khandaker, MRA;
Tariq, F;
Wong, KK;
Toding, A;
(2021)
Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning.
In:
Proceedings of the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring).
IEEE: Helsinki, Finland.
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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 complex
| Type: | Proceedings paper |
|---|---|
| Title: | Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning |
| Event: | 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) |
| ISBN-13: | 978-1-7281-8964-2 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/VTC2021-Spring51267.2021.9448826 |
| Publisher version: | https://doi.org/10.1109/VTC2021-Spring51267.2021.9... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Deep learning, Wireless communication, Vehicular and wireless technologies, Power demand, Simulation, Supervised learning, Receivers |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10133703 |
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