ORAN-MAP: A Hybrid Approach to Mobility-Aware Power Optimisation in Open Radio Access Networks (ORAN)

This paper presents a novel framework for optimising energy consumption in ORAN networks using machine learning (ML) models integrated with realistic mobility and spatial data aggregation techniques. The proposed approach leverages real-time Key Performance Metrics (KPMs) to dynamically manage the power states of Radio Units (RUs), ensuring energy efficiency while maintaining network performance. A dense urban simulation environment with realistic mobility patterns, based on a Poisson Point Process and Dijkstra’s Algorithm, models user movement and traffic dynamics. To address the challenges of large-scale dataset management, an H3 spatial indexing system aggregates data into hexagonal grids, reducing data size by 74% without sacrificing spatial accuracy. Five ML-based classifiers, including ensemble and regression-based methods, were trained and evaluated using the aggregated dataset based on actual data from the city of Leeds. The results demonstrate high accuracy for optimal power plans, with models achieving up to 97.8% accuracy. Network performance metrics, including throughput and energy efficiency, highlight significant improvements over a Full Power Baseline (FPB), with energy consumption reduced by up to 33.88% using the proposed models. These findings underscore the potential of ML-driven approaches to optimise energy usage in ORAN networks, providing a scalable and effective solution for sustainable network operations.