ML-Driven Energy Savings for Cellular Baseband Units via Traffic Prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0199009" target="_blank" >RIV/00216305:26220/26:0199009 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11062659" target="_blank" >https://ieeexplore.ieee.org/document/11062659</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/OJCOMS.2025.3584701" target="_blank" >10.1109/OJCOMS.2025.3584701</a>
Alternative languages
Result language
angličtina
Original language name
ML-Driven Energy Savings for Cellular Baseband Units via Traffic Prediction
Original language description
5G networks continue to expand as connected devices and traffic surge worldwide. This growth presents an opportune moment to address the ongoing challenge of Baseband Unit (BBU) energy consumption in large-scale deployments. Traditional static energy management approaches frequently waste resources and lead to increased costs, highlighting the need for more dynamic methods that adapt to changing network conditions. This paper introduces the Predictive Energy Saver for Baseband Units (PESBiU) 2.0, a new framework designed to address this challenge based on the premise that a balanced and advanced combination of precise traffic prediction and intelligent power-state decision-making can achieve superior energy savings without compromising user Quality of Service (QoS). PESBiU 2.0 uses granular interval datasets and machine learning (ML) models to predict traffic loads and optimize power states. The design features a hybrid architecture of Hyper Convolutional Neural Network-Long Short-Term Memory (Hyper-CNN-LSTM) model for accurate forecasting with a reinforcement learning (RL) decision engine based on Dueling Double Deep Q-Networks (DDDQN), making it the first framework to apply DDDQN for BBU energy optimization in 5G and beyond networks. Evaluation results confirm that PESBiU 2.0 effectively balances complexity and performance, achieving more than 40% reduction in BBU power consumption without compromising service quality. This benefits operators, researchers, and vendors seeking improved energy efficiency and consistent performance in 5G+ networks. The findings indicate a clear path for integrating advanced ML methods to enhance network efficiency and reliability, offering a scalable solution for future telecommunications.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Open Journal of the Communications Society
ISSN
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e-ISSN
2644-125X
Volume of the periodical
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Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
5759-5777
UT code for WoS article
001534517100002
EID of the result in the Scopus database
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