INGR Roadmap Energy Efficiency Chapter
Bibliographic record
Abstract
This 2021 Edition of the IEEE International Network Generations Roadmap (INGR) contains a new Chapter dedicated to Energy Efficiency, which builds upon the initial white paper released in April 2020 [1]. For this purpose, the Energy Efficiency Working Group developed an analysis of the energy efficiency constraints across the whole ecosystem of the Fifth Generation “5G” and following network infrastructure, which can be leveraged by all stakeholders to prioritize resources allocation and technology development to ensure that both technical and economic forecasts can be met. The complexity of the ecosystem and the traditionally siloed approach within the Industry has often prevented the adoption of a holistic approach to addressing the fundamental problem of energy, which is the ultimate constraint to any complex deployment. The proposed framework facilitates an assessment of bottlenecks and their implication on the network: it may be used by both academic and industry stakeholders to develop solutions that address the real issues and enable a healthy ecosystem. After a comprehensive survey of the ecosystem and its challenges, the following key areas were selected for a more in-depth analysis: •Network Efficiency •Small Cell Migration •Base Station Power •Economic Factors •Grid/Utility This Chapter also identifies the need for a comprehensive “Systems-of-Systems” (SoS) analysis to address the complex inter-relations among the multiple layers, which the infrastructure leverages. An initial proposal describes how a model can be built to enable a comprehensive assessment of energy requirements across such a diverse ecosystem. A future step in the process will consolidate a proposal for standardization of this model, which can be utilized by all stakeholders for both analysis and forecasting of capabilities and return on investment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.159 | 0.130 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".