A Survey of Energy Efficient Resource Management Techniques for Multicell Cellular Networks
Bibliographic record
Abstract
This paper surveys the recent findings in the area of energy efficient radio resource management in cellular networks. The primary objective is to identify and evaluate the key techniques that have the highest energy saving potential to be developed in the context of Green Networks while serving as a guideline for future research endeavours. The focus of the paper is targeted towards multicell networks which are composed of multiple BSs co-existing in the same area sharing the available radio resources. Due to this, greater emphasis is given towards the techniques that take inter-cell interference (ICI) into account while allocating the resources and, in the process, maximize the energy efficiency (EE). The resource management solutions presented in the paper are classified under three network domains namely homogeneous, heterogeneous, and cooperative networks. Furthermore, the analytical techniques for characterizing the EE of multicell networks are discussed in terms of the stochastic geometry framework. Finally, the paper outlines the current challenges and open issues in the area of energy efficient resource management for multicell cellular networks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".