AI-Enabled Cognitive Radio Systems: Balancing Energy Efficiency and Communication Performance

The rapid growth of wireless communication devices has intensified the need for efficient spectrum utilization and sustainable energy consumption. Cognitive Radio (CR) systems offer a promising solution by dynamically accessing underutilized frequency bands, but energy consumption remains a critical concern. This paper proposes an AI-enabled framework for cognitive radio networks that intelligently balances energy efficiency with communication performance. Leveraging machine learning algorithms, the system optimizes spectrum sensing, power allocation, and transmission scheduling to minimize power usage while maximizing throughput. Simulation results demonstrate that the proposed approach significantly reduces energy consumption without compromising data rates, highlighting its potential for green wireless communications. The integration of AI in CR networks paves the way for more adaptive, energy-aware, and high-performance communication systems.

Cognitive Radio, Energy Efficiency, Artificial Intelligence, Machine Learning, Spectrum Sensing, Power Optimization, Throughput Maximization, Green Wireless Communication

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