TY - JOUR
T1 - DRL-Based IRS-Assisted Secure Visible Light Communications
AU - Saifaldeen, Danya A.
AU - Ciftler, Bekir S.
AU - Abdallah, Mohamed M.
AU - Qaraqe, Khalid A.
N1 - Publisher Copyright:
© 2009-2012 IEEE.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - In this paper, we develop a novel Physical Layer Security (PLS) technique for a Visible Light Communication (VLC) system composed of light fixtures assisted by mirror array sheets serving as Intelligent Reflecting Surfaces (IRS). Our objective is to optimize the Secrecy Capacity (SC) by finding the optimal beamforming (BF) weights equipped at the VLC fixtures and mirror orientations at the mirror array sheet. Due to many design parameters, including the beamforming weights, the mirror orientations, and the mobility of the users, conventional optimization techniques may not be practical to optimize the SC capacity. Therefore, we proposed a Deep Reinforcement Learning (DRL) solution based on Deep Deterministic Policy Gradient (DDPG) algorithm to solve the highly complex SC problem by adjusting the BF weights and mirror orientations. The DDPG-based algorithm provides an optimized solution that can adapt to the large size of design parameters and act fast to the channel variations due to users' mobility. Our results show that considering both mirror array sheet and BF vectors provide the highest SC for the system. Moreover, we show the effect of changing the mirror arrangements of the mirror array sheet on SC. We conclude that for a fixed mirror array sheet size, there exists a specific mirror arrangement (i.e., number of mirrors) that optimizes the SC. After this number, the performance of SC saturates. We also show the trade-off between the training complexity and SC performance considering different mirror arrangements in the mirror array sheet.
AB - In this paper, we develop a novel Physical Layer Security (PLS) technique for a Visible Light Communication (VLC) system composed of light fixtures assisted by mirror array sheets serving as Intelligent Reflecting Surfaces (IRS). Our objective is to optimize the Secrecy Capacity (SC) by finding the optimal beamforming (BF) weights equipped at the VLC fixtures and mirror orientations at the mirror array sheet. Due to many design parameters, including the beamforming weights, the mirror orientations, and the mobility of the users, conventional optimization techniques may not be practical to optimize the SC capacity. Therefore, we proposed a Deep Reinforcement Learning (DRL) solution based on Deep Deterministic Policy Gradient (DDPG) algorithm to solve the highly complex SC problem by adjusting the BF weights and mirror orientations. The DDPG-based algorithm provides an optimized solution that can adapt to the large size of design parameters and act fast to the channel variations due to users' mobility. Our results show that considering both mirror array sheet and BF vectors provide the highest SC for the system. Moreover, we show the effect of changing the mirror arrangements of the mirror array sheet on SC. We conclude that for a fixed mirror array sheet size, there exists a specific mirror arrangement (i.e., number of mirrors) that optimizes the SC. After this number, the performance of SC saturates. We also show the trade-off between the training complexity and SC performance considering different mirror arrangements in the mirror array sheet.
KW - Deep reinforcement learning
KW - intelligent reflecting surfaces
KW - physical layer security
KW - secrecy capacity
KW - visible light communications
UR - http://www.scopus.com/inward/record.url?scp=85131727899&partnerID=8YFLogxK
U2 - 10.1109/JPHOT.2022.3178852
DO - 10.1109/JPHOT.2022.3178852
M3 - Article
AN - SCOPUS:85131727899
SN - 1943-0655
VL - 14
JO - IEEE Photonics Journal
JF - IEEE Photonics Journal
IS - 6
M1 - 8656209
ER -