Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach
The energy consumption of mobile networks poses a critical challenge. Mitigating this concern necessitates the deployment and optimization of network energy-saving solutions, such as carrier shutdown, to dynamically manage network resources. Traditional optimization approaches encounter complexity d...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10547043/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850035959048962048 |
|---|---|
| author | David Lopez-Perez Antonio De Domenico Nicola Piovesan Merouane Debbah |
| author_facet | David Lopez-Perez Antonio De Domenico Nicola Piovesan Merouane Debbah |
| author_sort | David Lopez-Perez |
| collection | DOAJ |
| description | The energy consumption of mobile networks poses a critical challenge. Mitigating this concern necessitates the deployment and optimization of network energy-saving solutions, such as carrier shutdown, to dynamically manage network resources. Traditional optimization approaches encounter complexity due to factors like the large number of cells, stochastic traffic, channel variations, and intricate trade-offs. This paper introduces the simulated reality of communication networks (SRCON) framework, a novel, data-driven modeling paradigm that harnesses live network data and employs a blend of machine learning (ML)- and expert-based models. These mix of models accurately characterizes the functioning of network components, and predicts network energy efficiency and user equipment (UE) quality of service for any energy carrier shutdown configuration in a specific network. Distinguishing itself from existing methods, SRCON eliminates the reliance on expensive expert knowledge, drive testing, or incomplete maps for predicting network performance. This paper details the pipeline employed by SRCON to decompose the large network energy efficiency modeling problem into ML- and expert-based submodels. It demonstrates how, by embracing stochasticity, and carefully crafting the relationship between such submodels, the overall computational complexity can be reduced and prediction accuracy enhanced. Results derived from real network data underscore the paradigm shift introduced by SRCON, showcasing significant gains over a state-of-the-art method used by a operator for network energy efficiency modeling. The reliability of this local, data-driven modeling of the network proves to be a key asset for network energy-saving optimization. |
| format | Article |
| id | doaj-art-0a366d79e922445f9f8cf7f8673be122 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-0a366d79e922445f9f8cf7f8673be1222025-08-20T02:57:19ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-01278080410.1109/TMLCN.2024.340769110547043Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based ApproachDavid Lopez-Perez0Antonio De Domenico1https://orcid.org/0000-0003-1229-4045Nicola Piovesan2https://orcid.org/0000-0001-5397-5248Merouane Debbah3https://orcid.org/0000-0001-8941-8080Institute of Telecommunications and Media Applications, Universitat Politècnica de València, Valencia, SpainHuawei Technologies, Boulogne-Billancourt, FranceHuawei Technologies, Boulogne-Billancourt, FranceKU 6G Research Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesThe energy consumption of mobile networks poses a critical challenge. Mitigating this concern necessitates the deployment and optimization of network energy-saving solutions, such as carrier shutdown, to dynamically manage network resources. Traditional optimization approaches encounter complexity due to factors like the large number of cells, stochastic traffic, channel variations, and intricate trade-offs. This paper introduces the simulated reality of communication networks (SRCON) framework, a novel, data-driven modeling paradigm that harnesses live network data and employs a blend of machine learning (ML)- and expert-based models. These mix of models accurately characterizes the functioning of network components, and predicts network energy efficiency and user equipment (UE) quality of service for any energy carrier shutdown configuration in a specific network. Distinguishing itself from existing methods, SRCON eliminates the reliance on expensive expert knowledge, drive testing, or incomplete maps for predicting network performance. This paper details the pipeline employed by SRCON to decompose the large network energy efficiency modeling problem into ML- and expert-based submodels. It demonstrates how, by embracing stochasticity, and carefully crafting the relationship between such submodels, the overall computational complexity can be reduced and prediction accuracy enhanced. Results derived from real network data underscore the paradigm shift introduced by SRCON, showcasing significant gains over a state-of-the-art method used by a operator for network energy efficiency modeling. The reliability of this local, data-driven modeling of the network proves to be a key asset for network energy-saving optimization.https://ieeexplore.ieee.org/document/10547043/Cellular networks5G6Genergy efficiencycarrier shutdownoptimization |
| spellingShingle | David Lopez-Perez Antonio De Domenico Nicola Piovesan Merouane Debbah Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach IEEE Transactions on Machine Learning in Communications and Networking Cellular networks 5G 6G energy efficiency carrier shutdown optimization |
| title | Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach |
| title_full | Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach |
| title_fullStr | Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach |
| title_full_unstemmed | Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach |
| title_short | Data-Driven Energy Efficiency Modeling in Large-Scale Networks: An Expert Knowledge and ML-Based Approach |
| title_sort | data driven energy efficiency modeling in large scale networks an expert knowledge and ml based approach |
| topic | Cellular networks 5G 6G energy efficiency carrier shutdown optimization |
| url | https://ieeexplore.ieee.org/document/10547043/ |
| work_keys_str_mv | AT davidlopezperez datadrivenenergyefficiencymodelinginlargescalenetworksanexpertknowledgeandmlbasedapproach AT antoniodedomenico datadrivenenergyefficiencymodelinginlargescalenetworksanexpertknowledgeandmlbasedapproach AT nicolapiovesan datadrivenenergyefficiencymodelinginlargescalenetworksanexpertknowledgeandmlbasedapproach AT merouanedebbah datadrivenenergyefficiencymodelinginlargescalenetworksanexpertknowledgeandmlbasedapproach |