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Hybrid-boosted model with an approach inspired by a mixture of experts for 5G energy consumption
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Authors: Rafael Sudbrack Zimmermann
Status: Final
Date of publication: 10 December 2024
Published in: ITU Journal on Future and Evolving Technologies, Volume 5 (2024), Issue 4, Pages 493-502
Article DOI : https://doi.org/10.52953/HSHZ4385
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Abstract:
The advancement of 5G networks has brought significant innovations in terms of services and technologies but this has also imposed challenges related to energy consumption. Over 70% of energy consumption was projected to be attributed to Radio Access Networks (RANs), specifically Base Stations (BSs), with data centers and fiber transport contributing to a lesser extent. The objective of this study was to optimize the parameters of BSs and energy-saving methods, providing a deep understanding of how these elements influence energy consumption. This study introduces a hybrid-boosted ensemble model tailored for predicting energy utilization in 5G base stations. The methodology merges ridge regression for linear trend analysis, XGBoost to tackle non-linear intricacies, and a final refinement strategy built upon a mixture of experts. Preliminary results demonstrate the model's capability to adjust to new and diverse data scenarios, achieving up to a 31.94% improvement in 5G energy forecasting compared to traditional models. This research constitutes a pivotal initiative in confronting adaptability hurdles in 5G energy predictions, setting a foundation for subsequent inquiries in this essential domain. |
Keywords: 5G, artificial intelligence, energy consumption, hybrid-boosted model, mixture of experts
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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