ML-Driven Energy Savings for Cellular Baseband Units via Traffic Prediction Article Swipe
Aneta Kolackova
,
Viet Anh Phan
,
Jan Jeřábek
,
Sergey Andreev
,
Jiří Hošek
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2025.3584701
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2025.3584701
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.
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Metadata
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ojcoms.2025.3584701
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https://openalex.org/W4411867024Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/ojcoms.2025.3584701Digital Object Identifier
- Title
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ML-Driven Energy Savings for Cellular Baseband Units via Traffic PredictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Aneta Kolackova, Viet Anh Phan, Jan Jeřábek, Sergey Andreev, Jiří HošekList of authors in order
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https://doi.org/10.1109/ojcoms.2025.3584701Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/ojcoms.2025.3584701Direct OA link when available
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Baseband, Computer science, Energy (signal processing), Real-time computing, Embedded system, Computer network, Statistics, Mathematics, Bandwidth (computing)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.2.0 | 109, 181 |
| abstract_inverted_index.40% | 190 |
| abstract_inverted_index.5G+ | 214 |
| abstract_inverted_index.BBU | 168, 193 |
| abstract_inverted_index.The | 127, 216 |
| abstract_inverted_index.and | 8, 40, 85, 92, 114, 123, 173, 185, 204, 210, 231 |
| abstract_inverted_index.can | 96 |
| abstract_inverted_index.for | 48, 65, 142, 167, 222, 237 |
| abstract_inverted_index.new | 71 |
| abstract_inverted_index.the | 20, 46, 61, 80, 161 |
| abstract_inverted_index.(ML) | 117 |
| abstract_inverted_index.(RL) | 149 |
| abstract_inverted_index.2.0, | 69 |
| abstract_inverted_index.Deep | 156 |
| abstract_inverted_index.This | 12, 58, 200 |
| abstract_inverted_index.Unit | 25 |
| abstract_inverted_index.lead | 41 |
| abstract_inverted_index.more | 49, 188 |
| abstract_inverted_index.need | 47 |
| abstract_inverted_index.path | 221 |
| abstract_inverted_index.than | 189 |
| abstract_inverted_index.that | 52, 82, 179 |
| abstract_inverted_index.this | 76 |
| abstract_inverted_index.user | 103 |
| abstract_inverted_index.uses | 110 |
| abstract_inverted_index.with | 145 |
| abstract_inverted_index.(BBU) | 26 |
| abstract_inverted_index.DDDQN | 166 |
| abstract_inverted_index.Hyper | 134 |
| abstract_inverted_index.Saver | 64 |
| abstract_inverted_index.Units | 67 |
| abstract_inverted_index.adapt | 53 |
| abstract_inverted_index.apply | 165 |
| abstract_inverted_index.based | 78, 152 |
| abstract_inverted_index.clear | 220 |
| abstract_inverted_index.first | 162 |
| abstract_inverted_index.loads | 122 |
| abstract_inverted_index.model | 141 |
| abstract_inverted_index.paper | 59 |
| abstract_inverted_index.power | 125, 194 |
| abstract_inverted_index.surge | 10 |
| abstract_inverted_index.waste | 38 |
| abstract_inverted_index.(QoS). | 107 |
| abstract_inverted_index.Double | 155 |
| abstract_inverted_index.Energy | 63 |
| abstract_inverted_index.Memory | 139 |
| abstract_inverted_index.Neural | 136 |
| abstract_inverted_index.PESBiU | 108, 180 |
| abstract_inverted_index.beyond | 174 |
| abstract_inverted_index.costs, | 44 |
| abstract_inverted_index.design | 128 |
| abstract_inverted_index.energy | 27, 34, 99, 169, 208 |
| abstract_inverted_index.engine | 151 |
| abstract_inverted_index.expand | 4 |
| abstract_inverted_index.future | 238 |
| abstract_inverted_index.growth | 13 |
| abstract_inverted_index.hybrid | 131 |
| abstract_inverted_index.making | 159 |
| abstract_inverted_index.models | 118 |
| abstract_inverted_index.moment | 17 |
| abstract_inverted_index.static | 33 |
| abstract_inverted_index.Dueling | 154 |
| abstract_inverted_index.Quality | 104 |
| abstract_inverted_index.Service | 106 |
| abstract_inverted_index.achieve | 97 |
| abstract_inverted_index.address | 19, 75 |
| abstract_inverted_index.confirm | 178 |
| abstract_inverted_index.devices | 7 |
| abstract_inverted_index.dynamic | 50 |
| abstract_inverted_index.enhance | 228 |
| abstract_inverted_index.machine | 115 |
| abstract_inverted_index.methods | 51, 226 |
| abstract_inverted_index.network | 56, 229 |
| abstract_inverted_index.ongoing | 21 |
| abstract_inverted_index.precise | 89 |
| abstract_inverted_index.predict | 120 |
| abstract_inverted_index.premise | 81 |
| abstract_inverted_index.results | 177 |
| abstract_inverted_index.savings | 100 |
| abstract_inverted_index.seeking | 206 |
| abstract_inverted_index.service | 198 |
| abstract_inverted_index.states. | 126 |
| abstract_inverted_index.traffic | 9, 90, 121 |
| abstract_inverted_index.vendors | 205 |
| abstract_inverted_index.without | 101, 196 |
| abstract_inverted_index.(DDDQN), | 158 |
| abstract_inverted_index.(PESBiU) | 68 |
| abstract_inverted_index.Baseband | 24, 66 |
| abstract_inverted_index.accurate | 143 |
| abstract_inverted_index.advanced | 86, 224 |
| abstract_inverted_index.balanced | 84 |
| abstract_inverted_index.balances | 183 |
| abstract_inverted_index.benefits | 201 |
| abstract_inverted_index.changing | 55 |
| abstract_inverted_index.continue | 2 |
| abstract_inverted_index.datasets | 113 |
| abstract_inverted_index.decision | 150 |
| abstract_inverted_index.designed | 73 |
| abstract_inverted_index.features | 129 |
| abstract_inverted_index.findings | 217 |
| abstract_inverted_index.granular | 111 |
| abstract_inverted_index.improved | 207 |
| abstract_inverted_index.indicate | 218 |
| abstract_inverted_index.interval | 112 |
| abstract_inverted_index.learning | 116, 148 |
| abstract_inverted_index.networks | 1 |
| abstract_inverted_index.offering | 233 |
| abstract_inverted_index.optimize | 124 |
| abstract_inverted_index.presents | 14 |
| abstract_inverted_index.quality. | 199 |
| abstract_inverted_index.scalable | 235 |
| abstract_inverted_index.solution | 236 |
| abstract_inverted_index.superior | 98 |
| abstract_inverted_index.achieving | 187 |
| abstract_inverted_index.challenge | 22, 77 |
| abstract_inverted_index.connected | 6 |
| abstract_inverted_index.framework | 72, 163 |
| abstract_inverted_index.increased | 43 |
| abstract_inverted_index.networks. | 175, 215 |
| abstract_inverted_index.opportune | 16 |
| abstract_inverted_index.reduction | 191 |
| abstract_inverted_index.resources | 39 |
| abstract_inverted_index.Evaluation | 176 |
| abstract_inverted_index.Predictive | 62 |
| abstract_inverted_index.Q-Networks | 157 |
| abstract_inverted_index.Short-Term | 138 |
| abstract_inverted_index.approaches | 36 |
| abstract_inverted_index.complexity | 184 |
| abstract_inverted_index.consistent | 211 |
| abstract_inverted_index.efficiency | 209, 230 |
| abstract_inverted_index.frequently | 37 |
| abstract_inverted_index.introduces | 60 |
| abstract_inverted_index.management | 35 |
| abstract_inverted_index.operators, | 202 |
| abstract_inverted_index.prediction | 91 |
| abstract_inverted_index.worldwide. | 11 |
| abstract_inverted_index.Traditional | 32 |
| abstract_inverted_index.combination | 87 |
| abstract_inverted_index.conditions. | 57 |
| abstract_inverted_index.consumption | 28, 195 |
| abstract_inverted_index.effectively | 182 |
| abstract_inverted_index.forecasting | 144 |
| abstract_inverted_index.integrating | 223 |
| abstract_inverted_index.intelligent | 93 |
| abstract_inverted_index.large-scale | 30 |
| abstract_inverted_index.performance | 212 |
| abstract_inverted_index.power-state | 94 |
| abstract_inverted_index.Network-Long | 137 |
| abstract_inverted_index.architecture | 132 |
| abstract_inverted_index.compromising | 102, 197 |
| abstract_inverted_index.deployments. | 31 |
| abstract_inverted_index.highlighting | 45 |
| abstract_inverted_index.optimization | 170 |
| abstract_inverted_index.performance, | 186 |
| abstract_inverted_index.reliability, | 232 |
| abstract_inverted_index.researchers, | 203 |
| abstract_inverted_index.Convolutional | 135 |
| abstract_inverted_index.reinforcement | 147 |
| abstract_inverted_index.decision-making | 95 |
| abstract_inverted_index.(Hyper-CNN-LSTM) | 140 |
| abstract_inverted_index.telecommunications. | 239 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8700000047683716 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.27065647 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |