Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network Operators
The urgent need to meet Environmental, Social, and Governance net-zero commitments and the financial risks posed by rising energy costs, are placing increasing pressure on Mobile Network Operators (MNOs) to optimise energy use. Given the significant energy consumption from Base Stations (BSs) and th...
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2025-01-01
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| author | Luis Mata Marco Sousa Pedro Vieira Maria Paula Queluz Antonio Rodrigues |
| author_facet | Luis Mata Marco Sousa Pedro Vieira Maria Paula Queluz Antonio Rodrigues |
| author_sort | Luis Mata |
| collection | DOAJ |
| description | The urgent need to meet Environmental, Social, and Governance net-zero commitments and the financial risks posed by rising energy costs, are placing increasing pressure on Mobile Network Operators (MNOs) to optimise energy use. Given the significant energy consumption from Base Stations (BSs) and the fact that Fifth Generation (5G) deployments are not meeting expectations for reduced energy consumption when compared to Fourth Generation (4G), MNOs are shifting from a load-centric optimisation approach to a service-centric paradigm that balances spectral efficiency and energy efficiency. This paper introduces the Energy Sustainability Framework, which provides a composite index to evaluate the spectral and energy efficiencies of BSs, summarised across four classes: A, B, C, and D. This framework supports network auditing, benchmarking, monitoring, and energy efficiency optimization while ensuring the desired Quality of Service (QoS). By leveraging Machine Learning (ML) and Explainable Artificial Intelligence (XAI) techniques applied to live data - including Performance Management (PM) and Energy Management (EM) metrics - integrated with domain expertise, the framework goes beyond deterministic computation to identify the root-cause factors influencing the BSs’ energy class, thereby enabling targeted optimisation. The framework can be flexibly applied at the location (site), BS (cell), or equipment type (hardware) levels. Notably, the implemented ML and XAI model achieved an F1-Score of 0.78 for 5G scenarios and 0.83 for 4G scenarios in predicting the Energy Sustainability Class, relying on a reduced set of indicators encompassing the most influential factors. An additional analysis, based on domain expertise, provided evidence of potential causal relationships between the class and observed variables involving channel quality, Multiple Input Multiple Output (MIMO) utilisation, network topology and energy-saving functionalities. For example, the results show that using higher order modulation coding schemes reduces the probability of a BS falling into class D by 50%. These findings provide practical insights for developing more sustainable network operations. |
| format | Article |
| id | doaj-art-fa4eb9c6e5de4fa7b70a752d0601225e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-fa4eb9c6e5de4fa7b70a752d0601225e2025-02-07T00:01:48ZengIEEEIEEE Access2169-35362025-01-0113223422236410.1109/ACCESS.2025.353628110857299Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network OperatorsLuis Mata0https://orcid.org/0000-0003-4572-9156Marco Sousa1https://orcid.org/0000-0002-2471-170XPedro Vieira2https://orcid.org/0000-0003-0279-8741Maria Paula Queluz3https://orcid.org/0000-0003-0266-4022Antonio Rodrigues4https://orcid.org/0000-0003-2115-7245Instituto de Telecomunicações, Lisbon, PortugalInstituto Superior de Engenharia de Lisboa, Lisbon, PortugalInstituto de Telecomunicações, Lisbon, PortugalInstituto de Telecomunicações, Lisbon, PortugalInstituto de Telecomunicações, Lisbon, PortugalThe urgent need to meet Environmental, Social, and Governance net-zero commitments and the financial risks posed by rising energy costs, are placing increasing pressure on Mobile Network Operators (MNOs) to optimise energy use. Given the significant energy consumption from Base Stations (BSs) and the fact that Fifth Generation (5G) deployments are not meeting expectations for reduced energy consumption when compared to Fourth Generation (4G), MNOs are shifting from a load-centric optimisation approach to a service-centric paradigm that balances spectral efficiency and energy efficiency. This paper introduces the Energy Sustainability Framework, which provides a composite index to evaluate the spectral and energy efficiencies of BSs, summarised across four classes: A, B, C, and D. This framework supports network auditing, benchmarking, monitoring, and energy efficiency optimization while ensuring the desired Quality of Service (QoS). By leveraging Machine Learning (ML) and Explainable Artificial Intelligence (XAI) techniques applied to live data - including Performance Management (PM) and Energy Management (EM) metrics - integrated with domain expertise, the framework goes beyond deterministic computation to identify the root-cause factors influencing the BSs’ energy class, thereby enabling targeted optimisation. The framework can be flexibly applied at the location (site), BS (cell), or equipment type (hardware) levels. Notably, the implemented ML and XAI model achieved an F1-Score of 0.78 for 5G scenarios and 0.83 for 4G scenarios in predicting the Energy Sustainability Class, relying on a reduced set of indicators encompassing the most influential factors. An additional analysis, based on domain expertise, provided evidence of potential causal relationships between the class and observed variables involving channel quality, Multiple Input Multiple Output (MIMO) utilisation, network topology and energy-saving functionalities. For example, the results show that using higher order modulation coding schemes reduces the probability of a BS falling into class D by 50%. These findings provide practical insights for developing more sustainable network operations.https://ieeexplore.ieee.org/document/10857299/Explainable artificial intelligencemachine learningwireless networks5Genergy efficiencyspectral efficiency |
| spellingShingle | Luis Mata Marco Sousa Pedro Vieira Maria Paula Queluz Antonio Rodrigues Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network Operators IEEE Access Explainable artificial intelligence machine learning wireless networks 5G energy efficiency spectral efficiency |
| title | Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network Operators |
| title_full | Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network Operators |
| title_fullStr | Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network Operators |
| title_full_unstemmed | Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network Operators |
| title_short | Optimizing Energy and Spectral Efficiency in Mobile Networks: A Comprehensive Energy Sustainability Framework for Network Operators |
| title_sort | optimizing energy and spectral efficiency in mobile networks a comprehensive energy sustainability framework for network operators |
| topic | Explainable artificial intelligence machine learning wireless networks 5G energy efficiency spectral efficiency |
| url | https://ieeexplore.ieee.org/document/10857299/ |
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