k-Nearest Neighbor Learning for Secure Intelligent Reflecting Surface Design

Yumou Chen, Muhammad R. A. Khandaker, Sami Azam*, Faisal Tariq, Risala T. Khan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationThe Fourth Industrial Revolution and Beyond
EditorsMd. Sazzad Hossain, Satya Prasad Majumder, Nazmul Siddique, Md. Shahadat Hossain
Number of pages15
ISBN (Electronic)9789811980329
ISBN (Print)9789811980312
Publication statusPublished - 3 Jun 2023
Event2021 International Conference on 4th Industrial Revolution and Beyond - Dhaka, Bangladesh
Duration: 10 Dec 202111 Dec 2021

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference2021 International Conference on 4th Industrial Revolution and Beyond
Abbreviated titleIC4IR 2021


  • 6G
  • DNN
  • KNN
  • Machine learning
  • Secrecy rate

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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