System Modeling and Deep Learning-Based Security Analysis of Uplink NOMA Relay Networks with IRS and Fountain Codes

Digital content such as games, extended reality (XR), and movies has been widely and easily distributed over wireless networks. As a result, unauthorized access, copyright infringement by third parties or eavesdroppers, and cyberattacks over these networks have become pressing concerns. Therefore, protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention. The Intelligent Reflecting Surface (IRS) is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment. This study investigates the security performance of an uplink Non-Orthogonal Multiple Access (NOMA) system integrated with an IRS and employing Fountain Codes (FCs). Specifically, two users send signals to the base station at separate distances. A relay receives the signal from the nearby user first and then relays it to the base station. The IRS receives the signal from the distant user and reflects it to the relay, which then sends the reflected signal to the base station. Furthermore, a malevolent eavesdropper intercepts both user and relay communications. We construct mathematical equations for Outage Probability (OP), throughput, diversity evaluation, and Interception Probability (IP), offering quantitative insights to assess system security and performance. Additionally, OP and IP are analyzed using a Deep Neural Network (DNN) model. A deeper comprehension of the security performance of the IRS-assisted NOMA system in signal transmission is provided by Monte Carlo simulations, which are also carried out to confirm the theoretical conclusions.