Cross-layer design of adaptive packet scheduling for green radio networks
Bibliographic record
Abstract
Introduction In a cellular wireless network, most of the energy is consumed in the radio access network [1]. Over the last decades, a significant amount of research work has focused on spectrally efficient and reliable wireless communications techniques at the physical (PHY) layer. However, the interactions among the layers (e.g. PHY-layer, radio link layer, and network layer) in the transmission protocol stack have to be taken into account to minimize the overall energy-consumption [2] in a green wireless network. The success of such a green wireless technology can be measured by energy-efficient metrics at different levels from the physical to application layer [3]. Energy efficiency across the entire system or network exploiting the layer interactions is notwell understood and needs more attention. The joint optimization of the transmission scheduling and resource allocation (or management) at various layers is referred to as cross-layer optimization. Again, energy efficiency in wireless communications systems so far has primarily focused on uplink communication due to the miniaturized mobile terminals and their limited energy storage capabilities. However, with a significant portion of the wireless internet traffic being from powerhungry base stations to end user mobile devices, energy optimization in the downlink is most important for green radio networks. In this article, we present a cross-layer optimized downlink packet transmission scheduling technique for the realization of green radio networks. After reviewing some related work on adaptive resource allocation in wireless networks, we discuss why cross-layer interaction, information exchange, and optimization are important for wireless networks, and more specifically, for green radio 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) | 0.000 | 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 | 0.001 |
| 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".