Power Adaptation for Optimizing Secrecy Energy Efficiency in NOMA-Enabled Underlay Cognitive Radio Networks and DNN-Based Evaluation

Physical layer security (PLS) has emerged as an innovative security measure for wireless networks, augmenting the prevailing cryptography-based methods. The concept of secrecy energy efficiency (SEE) efficiently tackles the challenge of integrating energy-efficient and secure communications. The combination of Non-orthogonal multiple access (NOMA) and cognitive radio (CR) has become a reliable solution for improving spectrum efficiency in wireless networks. This paper aims to analyze the SEE of the secondary network in NOMA-enabled underlay CR networks (NOMA-UCRNs), considering multiple non-colluding eavesdroppers. Firstly, analytical models are formulated to evaluate the SEE and secrecy sum rate (SSR) of the secondary network in NOMA-UCRN, considering residual hardware impairments, imperfect successive interference cancellation conditions, and interference power constraints at the primary receiver. Subsequently, joint optimal transmit power allocation (JOTPA) is ascertained for the secondary users (SUs) at the secondary transmitter and the secondary relay, with the aim of maximizing the SEE while satisfying constraints on tolerable interference power at the primary receiver and minimum data rates for the SUs. The computation of the JOTPA for the SUs is enabled by an iterative algorithm that utilizes the Dinkelbach method. It is demonstrated that the suggested JOTPA scheme significantly enhances the SEE of the secondary network in comparison to the random transmit power allocation and equal transmit power allocation strategies. Finally, a deep neural network (DNN) framework is developed as an innovative approach to accurately and quickly predict the JOTPA values that meet the desired objectives.