Secrecy Outage Probability Analysis for RIS-Assisted NOMA Systems
July 31, 2020 Β· Declared Dead Β· π Electronics Letters
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Liang Yang, Yongjie Yuan
arXiv ID
2007.15902
Category
eess.SP: Signal Processing
Cross-listed
cs.CR
Citations
45
Venue
Electronics Letters
Last Checked
6 months ago
Abstract
In this paper, the physical layer security (PLS) for a novel reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system in a multi-user scenario is investigated, where we consider the worst case that the eavesdropper also utilizes the advantage of the RISs. More specifically, we derive analytical results for the secrecy outage probability (SOP). From the numerical results, we observe that the use of RISs can improve the secrecy performance compared to traditional NOMA systems. However, for the worst case that the received signals at the eavesdropper comes from the RISs and source, increasing the number of intelligent elements on the RIS has a negative impact on the secrecy performance. At high SNRs, the system's SOP tends to a constant. Finally, the secrecy performance can be improved through the group selection.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Signal Processing
R.I.P.
π»
Ghosted
π
π
The Cartographer
1D Convolutional Neural Networks and Applications: A Survey
R.I.P.
π»
Ghosted
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
π
π
The Cartographer
Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond
R.I.P.
π»
Ghosted
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
R.I.P.
π»
Ghosted
A New Wireless Communication Paradigm through Software-controlled Metasurfaces
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted