Research on Resource Allocation in Cognitive Radio Networks Assisted by IRS.

Area of Science:

• Wireless communication networks
• Resource allocation optimization
• Signal processing

Background:

• Severe signal attenuation in long-distance transmissions reduces energy efficiency in cognitive radio networks.
• Underlay mode cognitive radio networks face challenges in balancing secondary user performance with primary user interference constraints.

Purpose of the Study:

• To develop an intelligent reflecting surface (IRS)-assisted, energy-constrained relay cognitive radio resource allocation model.
• To maximize the energy efficiency of secondary users while ensuring primary user interference constraints are met.
• To improve overall system performance and communication quality in cognitive radio networks.

Main Methods:

• Construction of an IRS-assisted and energy-constrained relay cognitive radio resource allocation model.
• Introduction of controllable reflective paths to enhance link quality and energy utilization.
• Development and application of a Chaotic Spider Wasp Optimization algorithm for solving the complex optimization problem.
• Incorporation of the Jain fairness index for equitable power allocation among secondary users.

Main Results:

• The proposed model significantly improves system energy efficiency.
• The optimization method enhances the stability of communication quality.
• The Chaotic Spider Wasp Optimization algorithm effectively addresses the non-convex and high-dimensional nature of the problem, avoiding premature convergence.

Conclusions:

• The IRS-assisted resource allocation model is effective in mitigating signal attenuation and improving energy efficiency in cognitive radio networks.
• The proposed optimization algorithm provides a robust solution for complex resource allocation problems, ensuring fairness and performance.
• This research offers a significant advancement in optimizing cognitive radio network performance under energy constraints.