AI-Empowered Multivariate Probabilistic Forecasting: A Key Enabler for Sustainability in Open RAN

This paper explores the role of multivariate probabilistic forecasting in improving O-RAN operations, focusing on network sustainability aspects. A comprehensive analysis of its potential benefits and challenges, as well as its integration into the O-RAN architecture are described. The paper first presents an overview of the O-RAN architecture and components, followed by an examination of power consumption models relevant to O-RAN deployments and the challenges associated with traditional deterministic models in resource allocation. We then examine the performance of several state-of-the-art probabilistic multivariate forecasting techniques namely, Gaussian Process Vector Autoregression (GPVAR), Temporal Fusion Transformer (TFT) and non-probabilistic multivariate technique namely, Multivariate Long-Short Term Memory (LSTM) and explain their implementation details and provide their evaluations. The simulation results show the effectiveness of these techniques in predicting Physical Resource Block (PRB) utilization and optimizing resource allocation. In particular, significant energy savings – around 20-30%– are achieved, depending on the percentile of the used probabilistic forecasting techniques. The benefits of considering probabilistic forecasting techniques compared to multi-variate LSTM are also analyzed. Our results emphasize the potential of probabilistic forecasting to improve energy efficiency and sustainability in O-RAN operations.