The Open Radio Access Network (Open RAN) is an emerging idea -- transforming the traditional Radio Access Networks (RAN) that are monolithic and inflexible into more flexible and innovative. By leveraging open standard interfaces, data collection across all RAN layers becomes feasible, paving the way for the development of energy-efficient Open RAN architectures through Artificial Intelligence / Machine Learning (AI/ML). However, the inherent complexity and black-box nature of AI/ML models used for energy consumption prediction pose challenges in interpreting their underlying factors and relationships. This work presents an integration of eXplainable AI (XAI) to understand the key RAN parameters that contribute to energy consumption. Furthermore, the paper delves into the analysis of RAN parameters -- \emph{airtime}, \emph{goodput}, \emph{throughput}, \emph{buffer status report}, \emph{number of resource blocks}, and many others -- identified by XAI techniques, highlighting their significance in energy consumption.
@article{arxiv.2504.18029,
title = {Integrating Explainable AI for Energy Efficient Open Radio Access Networks},
author = {L. Malakalapalli and V. Gudepu and B. Chirumamilla and S. J. Yadhunandan and K. Kondepu},
journal= {arXiv preprint arXiv:2504.18029},
year = {2025}
}