ML models predict O-RAN power consumption with high accuracy · 1 source tracked

Researchers have developed machine learning models to predict power consumption in virtualised open radio access networks (O-RANs), addressing the need for energy efficiency in dynamic, software-defined environments. A hybrid model combining deep neural networks (DNNs) for feature extraction with an XGBoost regressor demonstrated superior performance. This DNN-XGBoost model achieved a mean relative error below 0.5% across various system parameters, suggesting its potential integration into O-RAN management tools for enhanced energy efficiency. AI

IMPACT This research could lead to more energy-efficient network orchestration in future communication systems.

RANK_REASON Academic paper detailing a new ML methodology for a specific technical problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• deep neural network
• O-RANs
• XGBoost

• paper
• infra

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