Towards an energy efficient 5G Network: Integrating artificial intelligence with RF circuit design

Abstract

As 5G networks expand, managing energy consumption has become a significant challenge. This study investigates the integration of Artificial Intelligence (AI) with Radio Frequency (RF) circuit design to enhance energy efficiency without compromising performance. By leveraging machine learning and deep learning algorithms, we optimize RF parameters such as power scaling, beamforming, and fault prediction. Our findings show that AI-driven optimization can reduce power consumption by up to 35%, improve beamforming efficiency by 15%, and reduce system downtimes by 22% through fault prediction. Additionally, dynamic power scaling techniques contribute to a 28% increase in energy efficiency. These results demonstrate the potential of AI to drive significant improvements in 5G infrastructure, offering a path toward more sustainable, high-performance networks. Further advancements in real-time AI optimization can further enhance energy savings, providing a foundation for future energy-efficient 5G networks.