Optimization of Signal to Noise Ratio and Power Consumption of Cognitive Radio Systems using Enhanced PSO Algorithm

Abstract

This paper demonstrated the importance of interdisciplinary collaboration, combining knowledge from telecommunications, optimization algorithms, and engineering principles. It underscores the need for adaptable and intelligent cognitive radio engines that dynamically adjust to varying network conditions, ensuring efficient spectrum utilization and reliable communication services. Cognitive Radio (CR) is basically focused on building a flexible and reconfigurable radio guided by intelligent sensors. The sensors are responsible for sensing the cognitive radio environment, learning from previous experience and knowledge and adopting this information and its transmission parameters such as transmit power, modulation type and modulation index for enhanced spectrum usage. The CR engine then coordinates these sensors and optimizes the routine for learning and efficient decision-making. The major challenges of CR are real-time adoption of transmission parameters, along with the environmental information such as transmission distance, noise power and available spectrum. Through the optimization process, we have uncovered Pareto-optimal solutions that represent the best compromises between these objectives. These solutions provide a valuable resource for designers and engineers to make informed decisions, selecting the most appropriate radio engine configurations that align with their specific requirements and operational environments. The performance analysis of the enhanced Particle Swarm Optimization shows ePSO's potential to tackle the intricate optimization tasks associated with cognitive radio design, establishing a foundation for more dependable and versatile wireless communication systems.