Maximizing Spectrum Utilization and Energy Efficiency in OFDMA Networks through Subcarrier Allocation and Power Control Optimization

As the demand for efficient spectrum utilization and energy efficiency in wireless communication systems grows, Orthogonal Frequency Division Multiple Access (OFDMA) has emerged as a promising technique. Current optimization methods, including Genetic Algorithms, Machine Learning, and the Waterfilling Algorithm, show potential for enhancing performance but face significant challenges. This study investigates optimizing Orthogonal Frequency Division Multiple Access (OFDMA) systems with enhanced complexity, including 10 users, 256 subcarriers, and a system bandwidth of 100 MHz. The system operates at a higher Signal-to-Noise Ratio (SNR) of 20 dB, using 16-QAM modulation and a power constraint of 2 per user. The proposed framework combines adaptive subcarrier allocation and power control to optimize spectrum utilization and energy efficiency under these conditions. Simulation results demonstrate the efficacy of the approach, achieving an average spectrum utilization of 0.69141 and an average energy efficiency of 1.4447. Compared to traditional methods, this framework significantly enhances both metrics, providing a more robust and efficient solution for OFDMA systems. These findings underline the potential of the proposed framework in improving scalability and adaptability, addressing the challenges of dynamic channel conditions, user mobility, and concurrent optimization of spectrum and energy efficiency. By bridging simulation results with real-world applicability, this approach contributes to the development of advanced cognitive radio networks, ensuring better spectrum access and reduced interference in large-scale environments.