TENORAN: Automating Fine-grained Energy Efficiency Profiling in Open RAN Systems
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866915797003141120 |
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| author | Shirkhani, Ravis Maxenti, Stefano Bonati, Leonardo Mohamadi, Niloofar Elkael, Maxime Hashmi, Umair Mitra, Jeebak Polese, Michele Melodia, Tommaso D'Oro, Salvatore |
| author_facet | Shirkhani, Ravis Maxenti, Stefano Bonati, Leonardo Mohamadi, Niloofar Elkael, Maxime Hashmi, Umair Mitra, Jeebak Polese, Michele Melodia, Tommaso D'Oro, Salvatore |
| contents | The transition to disaggregated and interoperable Open Radio Access Network (RAN) architectures and the introduction of RAN Intelligent Controllers (RICs) in O-RAN creates new resource optimization opportunities and fine-grained tuning and configuration of network components to save energy while fulfilling service demand. However, unlocking this potential requires fine-grained and accurate energy measurements across heterogeneous deployments. Three factors make this particularly challenging [...]. To address these challenges, we design the TENORAN framework, an automated measurement scaffold for fine-grained energy efficiency profiling of O-RAN deployments, and prototype it on a heterogeneous OpenShift cluster. TENORAN instruments an end-to-end deployment based on high-level specifications (e.g., gNB software stack and split options, traffic profiles), and collects synchronized performance metrics and power measurements for individual RAN components while the network is under controlled workloads including over-the-air traffic. Our experimental results demonstrate energy profiling of end-to-end experiments with xApps in the loop, energy efficiency differences between two RAN stacks, OpenAirInterface and srsRAN, in uplink and downlink, and core network power consumption trends. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13085 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TENORAN: Automating Fine-grained Energy Efficiency Profiling in Open RAN Systems Shirkhani, Ravis Maxenti, Stefano Bonati, Leonardo Mohamadi, Niloofar Elkael, Maxime Hashmi, Umair Mitra, Jeebak Polese, Michele Melodia, Tommaso D'Oro, Salvatore Networking and Internet Architecture The transition to disaggregated and interoperable Open Radio Access Network (RAN) architectures and the introduction of RAN Intelligent Controllers (RICs) in O-RAN creates new resource optimization opportunities and fine-grained tuning and configuration of network components to save energy while fulfilling service demand. However, unlocking this potential requires fine-grained and accurate energy measurements across heterogeneous deployments. Three factors make this particularly challenging [...]. To address these challenges, we design the TENORAN framework, an automated measurement scaffold for fine-grained energy efficiency profiling of O-RAN deployments, and prototype it on a heterogeneous OpenShift cluster. TENORAN instruments an end-to-end deployment based on high-level specifications (e.g., gNB software stack and split options, traffic profiles), and collects synchronized performance metrics and power measurements for individual RAN components while the network is under controlled workloads including over-the-air traffic. Our experimental results demonstrate energy profiling of end-to-end experiments with xApps in the loop, energy efficiency differences between two RAN stacks, OpenAirInterface and srsRAN, in uplink and downlink, and core network power consumption trends. |
| title | TENORAN: Automating Fine-grained Energy Efficiency Profiling in Open RAN Systems |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2602.13085 |