Achieving 5G Network Energy Efficiency by Hybrid Machine Learning Models for Channel and Power Allocation

High data speeds, low latency, and huge connection are becoming increasingly important in 5G networks, making energy efficiency a significant concern. In order to achieve the twin objectives of lowering energy consumption and keeping network performance intact, our research centers on creating hybrid machine learning models to improve channel and power allocation. 5G networks are complex and dynamic, with varying user requirements, fluctuating traffic loads, and different environmental circumstances. Traditional resource allocation algorithms have a hard time keeping up. The authors of this work offer a hybrid strategy that is both adaptable and scalable by integrating supervised and unsupervised learning with reinforcement learning (RL). To begin, the hybrid model uses supervised learning to allocate channels and power using past network data. Next, reinforcement learning takes into account network circumstances, user mobility, and traffic variations to make real-time adjustments to the allocation. To improve the distribution of resources in dense networks, unsupervised learning is used to find clusters and patterns in the data. The system is able to optimize energy consumption and anticipate and respond to changes in the network environment thanks to the integration of these models. With throughput and quality of service (QoS) unaffected, the simulation results show that the hybrid machine learning model is 30% more energy efficient than traditional techniques. In light of increasing data demands and strict energy requirements, the suggested technology provides a viable option for 5G networks that are both environmentally friendly and highly efficient.