Enhancing Spectrum Utilization and Energy Efficiency in OFDMA Networks through Optimized Subcarrier Allocation and Power Control

The increasing demand for efficient spectrum utilization and energy conservation in wireless communication systems has highlighted Orthogonal Frequency Division Multiple Access (OFDMA) as a key enabling technology. However, existing optimization techniques, such as Genetic Algorithms, Machine Learning-based methods, and the Water-filling Algorithm, face challenges related to scalability and computational complexity. This study presents an optimized framework for enhancing spectrum utilization and energy efficiency in OFDMA networks, modelled with 10 users, 256 subcarriers, a system bandwidth of 100 MHz, and operating at a Signal-to-Noise Ratio (SNR) of 20 dB. Utilizing 16-QAM modulation and adhering to a per-user power constraint of 2 units, the proposed framework integrates adaptive subcarrier allocation and power control. The methodology employs simulation-based analysis to evaluate performance under realistic network conditions. Results demonstrate significant improvements, achieving an average spectrum utilization of 0.69141 and an average energy efficiency of 1.4447, indicating the method’s effectiveness in maximizing resource allocation while minimizing energy consumption. This research bridges theoretical models with practical applications, contributing to the advancement of cognitive radio networks by providing a scalable and efficient solution for spectrum access. The proposed approach reduces interference and enhances operational efficiency, offering valuable insights for the deployment of large-scale OFDMA systems in next-generation communication networks.