Performance Analysis of CR‐NOMA Network With Gray Wolf Optimization and Intelligent Reflecting Surface

ABSTRACT Efficient spectrum management is a key challenge in environments with limited spectrum availability. Cognitive radio (CR) offers a promising solution by enabling dynamic spectrum access. However, its success depends heavily on the effectiveness of spectrum sensing and data transmission based on these sensing results. This paper introduces a novel approach that explores the performance of a gray wolf optimization (GWO)‐assisted intelligent reflecting surface (IRS) for spectrum sensing and data transmission in a cognitive radio network (CRN). We integrate non‐orthogonal multiple access (NOMA) and energy harvesting (EH) to boost spectral efficiency in energy‐constrained environments. Our goal is to enhance the spectrum sensing performance of battery‐operated unlicensed users (BOU) and improve data transmission efficiency for multiple NOMA users using the GWO‐assisted IRS. For optimizing the IRS phase for multiple NOMA users, we propose partitioning the IRS into distinct groups for each user. We validate the effectiveness of this integrated approach through simulations, presenting results including receiver operating characteristics (ROC) curves for spectrum sensing, the relationship between harvested power and received power for energy harvesting, and metrics such as outage probability and throughput to assess spectral efficiency.