@inproceedings{09e3792b03a94f7bbeac89030788ac0c,
title = "k-Nearest Neighbor Learning for Secure Intelligent Reflecting Surface Design",
abstract = "Recently, intelligent reflecting surfaces (IRSs) have seen an upsurge of interest due to their ability to make the wireless environment programmable, which has historically been treated as an uncontrollable natural phenomenon. Even passive IRSs consisting of many reflecting units can autonomously adjust the reflection coefficients to alter the phase and amplitude of the incident signals. However, optimal reflection design for large IRSs are deemed to be impractical due to the underlying computational complexity. In this paper, we design IRS-assisted programmable wireless environment for secure communication using deep learning techniques. More specifically, we consider the k-nearest neighbor learning algorithm for significantly reducing the computational complexity in IRS design. Simulation results demonstrate the effectiveness of the proposed deep learning-based solutions as compared with traditional alternating optimization.",
keywords = "6G, DNN, KNN, Machine learning, Secrecy rate",
author = "Yumou Chen and Khandaker, {Muhammad R. A.} and Sami Azam and Faisal Tariq and Khan, {Risala T.}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 2021 International Conference on 4th Industrial Revolution and Beyond, IC4IR 2021 ; Conference date: 10-12-2021 Through 11-12-2021",
year = "2023",
month = jun,
day = "3",
doi = "10.1007/978-981-19-8032-9_15",
language = "English",
isbn = "9789811980312",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer",
pages = "197--211",
editor = "Hossain, {Md. Sazzad} and Majumder, {Satya Prasad} and Nazmul Siddique and Hossain, {Md. Shahadat}",
booktitle = "The Fourth Industrial Revolution and Beyond",
}