Scheduling vs. pseudo-scheduling models in IEEE 802.16j wireless relay networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it