A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO.

Area of Science:

• Wireless Communication
• Signal Processing
• Optimization Theory

Background:

• Traditional cognitive radio power allocation algorithms often neglect channel fluctuations and fairness among users.
• Complexity in existing algorithms hinders the identification of optimal power allocation schemes.

Purpose of the Study:

• To develop a robust power allocation model for cognitive radio systems that improves spectral utilization and user fairness.
• To address the limitations of traditional algorithms by incorporating channel variability and worst-case channel models.

Main Methods:

• A robust model was established by adding a cognitive user transmission rate variance constraint.
• The maximum channel capacity time power allocation scheme was solved using a worst-case channel transmission model.
• A hybrid particle swarm algorithm was employed to solve the non-convex optimization problem.

Main Results:

• The proposed algorithm demonstrates good robustness and enhances fairness among cognitive users.
• It effectively utilizes channel resources under defined constraints.
• The algorithm exhibits simplicity, fast convergence, and achieves good optimization results.

Conclusions:

• The developed cognitive radio power allocation algorithm offers a robust and efficient solution for improving spectral utilization and user fairness.
• The hybrid particle swarm optimization approach effectively tackles complex non-convex problems in power allocation.