Scheduling vs. pseudo-scheduling models in IEEE 802.16j wireless relay networks
Bibliographic record
Abstract
Given the relatively low costs associated with its deployment and its capacity to deliver last mile wireless broadband access, WiMAX and LTE are the current technologies of choice to effectively meet the increasing demand for high bandwidth services and applications. In the literature, several pseudo scheduling algorithms, which are very often time-independent, have been proposed to optimize the scheduling horizon without taking care of the sequencing of packets within the scheduling period. In this paper, we develop an optimization model having in mind to perform "true" scheduling, not only optimizing the scheduling horizon but taking into account the allocation of resources over a given time window. We propose a two-step solution scheme. The first step relies on a model, which chooses among a set of possible configurations (a set of transmitting links over a predetermined period of time slots) with end-to-end transmissions. The second step consists in a time ordering of those configurations, in order to complete the scheduling process. In our experiments, we compare our solution scheme with one of those so-called "scheduling" in order to investigate how the throughput varies depending on whether we use pseudo=scheduling vs. "true" scheduling. The results show how explicitly considering nodal buffers can make a meaningful difference on the forms of scheduling, depending on the assumptions on the buffer sizes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".