Energy Efficiency Architecture Design for Heterogeneous Cellular Networks
Bibliographic record
Abstract
Abstract Heterogeneous cellular networks (HetNets) have emerged as a new promising paradigm to further enhance capacity, where multiple types of low power smallcells are overlaid in a high power macrocell. They provide more opportunities to explore the potential cognition and cooperation diversities to improve the spectral efficiency. On the other hand, energy efficiency is a critical performance metric, which deserves more attention from academia, industry, and standardization, in particular, in scenarios where smallcells are densely deployed. In this paper, a systematic architecture is presented to efficiently utilize network resources and thus improve the overall energy efficiency. The architecture is referred to as OCRT because it combines a multi‐tier energy efficiency considerations of operators, core networks, radio access networks and terminals (e.g., OCRT). Furthermore, a corresponding triply‐cycle‐based functional structure is proposed for the OCRT to make various interactions between the corresponding functional entities clear. An implementation scheme of OCRT based on cognitive information interaction cycle and an energy efficiency‐aware protocol is presented. Finally, a use case of the presented OCRT green design is provided for energy efficiency optimization in a cognition‐and‐cooperation‐characterized HetNet. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".