TY - JOUR
T1 - Secrecy rate maximization for hardware impaired untrusted relaying network with deep learning
AU - Bastami, Hamed
AU - Moradikia, Majid
AU - Behroozi, Hamid
AU - de Lamare, Rodrigo C.
AU - Abdelhadi, Ahmed
AU - Ding, Zhiguo
N1 - Funding Information:
Hamid Behroozi (S’04-M’08) received the B.Sc. degree in Electrical Engineering from the University of Tehran, Tehran, Iran, in 2000, the M.Sc. degree in Electrical Engineering from Sharif University of Technology, Tehran, in 2003, and the Ph.D. degree in Electrical Engineering from Concordia University, Montreal, QC, Canada, in 2007. From 2007 to 2010, he was a Postdoctoral Fellow with the Department of Mathematics and Statistics, Queens University, Kingston, ON, Canada. He is currently an Associate Professor with the Department of Electrical Engineering, Sharif University of Technology, Tehran. His research interests include information theory, joint source-channel coding, artificial intelligence in signal processing and data science, and cooperative communications. Dr. Behroozi was the recipient of several academic awards, including Ontario Postdoctoral Fellowship awarded by the Ontario Ministry of Research and Innovation (MRI), Quebec Doctoral Research Scholarship awarded by the Government of Quebec (FQRNT), Hydro Quebec Graduate Award, and Concordia University Graduate Fellowship.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/12
Y1 - 2021/12
N2 - This paper investigates the physical layer security (PLS) design of an untrusted relaying network where the source node coexists with a multi-antenna eavesdropper (Eve). While the communication relies on untrustworthy relay nodes to increase reliability, we aim to protect the confidentiality of information against combined eavesdropping attacks performed by both untrusted relay nodes and Eve. Considering the hardware impairments (HIs), both total power budget constraint for the whole network and the individual power constraint at each node, this paper presents a novel approach to jointly optimize relay beamformer and transmit powers aiming at maximizing average secrecy rate (ASR). To safeguard the first cooperative phase, destination-aided cooperative jamming (DACJ) is employed, while for the second phase, the relay beamformer is adjusted. The resultant optimization problem is non-convex, and a suboptimal solution is obtained through the sequential parametric convex approximation (SPCA) method. In order to prevent any failure due to infeasibility, we propose an iterative initialization algorithm to find the feasible initial point of the original problem instead of an arbitrary point, as in the conventional SPCA. To satisfy low-latency as one of the main key performance indicators (KPI) required in beyond 5G (B5G) communications, a computationally efficient data-driven approach is developed exploiting a deep learning model to evaluate the proposed scheme while the computational burden is significantly reduced. Simulation results assess the effect of different system parameters on the ASR performance as well as the effectiveness of the proposed deep learning solution in large-scale cases.
AB - This paper investigates the physical layer security (PLS) design of an untrusted relaying network where the source node coexists with a multi-antenna eavesdropper (Eve). While the communication relies on untrustworthy relay nodes to increase reliability, we aim to protect the confidentiality of information against combined eavesdropping attacks performed by both untrusted relay nodes and Eve. Considering the hardware impairments (HIs), both total power budget constraint for the whole network and the individual power constraint at each node, this paper presents a novel approach to jointly optimize relay beamformer and transmit powers aiming at maximizing average secrecy rate (ASR). To safeguard the first cooperative phase, destination-aided cooperative jamming (DACJ) is employed, while for the second phase, the relay beamformer is adjusted. The resultant optimization problem is non-convex, and a suboptimal solution is obtained through the sequential parametric convex approximation (SPCA) method. In order to prevent any failure due to infeasibility, we propose an iterative initialization algorithm to find the feasible initial point of the original problem instead of an arbitrary point, as in the conventional SPCA. To satisfy low-latency as one of the main key performance indicators (KPI) required in beyond 5G (B5G) communications, a computationally efficient data-driven approach is developed exploiting a deep learning model to evaluate the proposed scheme while the computational burden is significantly reduced. Simulation results assess the effect of different system parameters on the ASR performance as well as the effectiveness of the proposed deep learning solution in large-scale cases.
KW - Deep learning
KW - Hardware impairments
KW - Passive eavesdropper
KW - Physical layer security
KW - Untrusted relay
UR - http://www.scopus.com/inward/record.url?scp=85118477537&partnerID=8YFLogxK
U2 - 10.1016/j.phycom.2021.101476
DO - 10.1016/j.phycom.2021.101476
M3 - Article
AN - SCOPUS:85118477537
SN - 1874-4907
VL - 49
JO - Physical Communication
JF - Physical Communication
M1 - 101476
ER -