A multi-objective function for deep learning-based automatic energy efficiency power allocation in multicarrier noma system using hybrid heuristic improvement

Non-Orthogonal Multiple Access (NOMA) is the successive multiple-access methodologies for modern communication devices. Energy Efficiency (EE) is suggested in the NOMA system. In dynamic network conditions, the consideration of NOMA shows high computational complexity that minimizes the EE to degrade the system performance. This research suggested EE for the Multi-Carrier NOMA (MC-NOMA) models by optimization algorithm. The main scope of this research tends to improve the EE by Hybrid of Sewing Training and Lemur Optimization for optimizing the system parameters. The improvement made in this developed HSTLO algorithm can provide significant impact on MC-NOMA system, which it renders better user capacity while effectively optimizing the system parameters. Moreover, the Dilated Dense Recurrent Neural Network (DDRNN) model is developed. Employing the improvement in the deep learning model for the MC-NOMA system could effectively manage and enhance the system performance. Considering the DDRNN model can leverage to provide better generalization outcomes in different network scenarios that ensures to provide fast and reliable solutions compared to existing methods. Addressing the energy consumption problems in this research study will be analysed to show the advancement in MC-NOMA system that help to enhance the system performance.