Research on Resource Allocation in Cognitive Radio Networks Assisted by IRS

To address the reduction in energy efficiency caused by severe signal attenuation during long-distance transmission in cognitive radio networks, this paper constructs an IRS-assisted and energy-constrained relay cognitive radio resource allocation model operating in the underlay mode. By introducing controllable reflective paths, the model enhances link quality and improves energy utilization efficiency. Our objective is to maximize the energy efficiency of secondary users while satisfying the interference constraints imposed on the primary user. To effectively solve the highly non-convex and high-dimensional optimization problem, we propose a Chaotic Spider Wasp Optimization algorithm. The algorithm employs chaotic mapping to initialize the population and enhance population diversity, and incorporates a dynamic trade-off factor to achieve an adaptive balance between hunting and nesting behaviors, thereby improving global search capability and avoiding premature convergence. In addition, the Jain fairness index is introduced to enforce fairness in the power allocation among secondary users. Simulation results demonstrate that the proposed model and optimization method significantly improve system energy efficiency and the stability of communication quality.