Medical Sensor Data Security: A DNN Framework for SOP Prediction in Two-Way Relay NOMA Systems

Abstract

Securing medical sensor data are imperative due to the susceptibility of wireless transmissions to eavesdropping. In this letter, we focus on improving the security of two-way communication in medical networks by investigating deep neural networks (DNN) for two-way (TWR) relay nonorthogonal multiple access (NOMA) systems. Utilizing a decode-and-forward (DF) relay and considering both maximum ratio combining and selection combining at the eavesdropper, we derive analytical expressions for the secrecy outage probability (SOP), leveraging the exact SOP expression from (Shukla et al., 2020). Due to the system's complexity, deriving a closed-form SOP is challenging. To address this, we introduce a DNN framework for real-time SOP prediction, which not only validates the theoretical model but also significantly reduces offline execution time and computational complexity.