Routing and Scheduling Using Column Generation in IEEE 802.16j Wireless Relay Networks
Bibliographic record
Abstract
Worldwide Interoperability for Microwave Access (WiMAX) has become an important standard in wireless telecommunication networks in recent years due to the increasing bandwidth requirements, as well as to customer demand for having ubiquitous access to the network. One of the most recent versions of WiMAX is IEEE 802.16-2009, but in this thesis we work with its 802.16j amendment. This amendment includes the use of relay stations (RS) to improve the network's throughput, with the RSs becoming intermediaries between the base station (BS) and the subscriber stations (SS). \n \nIn the literature, there have been several authors claiming to perform joint routing and scheduling in wireless networks using the column generation technique. Nevertheless, these papers are not performing scheduling since they do not specify how time slots are allocated to each transmitting node over time (they only count the time slots it takes to transmit data). \n \nThat is why we developed an optimization model (that is solved using column generation) having in mind the fact of performing real scheduling, not only counting time slots but taking into account the allocation of resources over a period of time. The model we developed chooses among a set of possible configurations (a set of transmitting links over a predetermined period of time slots) to calculate the time it takes to transmit data from end to end. \n \nAfter obtaining some simulation results with our model, we compared them with those of a model that does not perform real scheduling. The results show only minor differences in the total number of time slots that a transmission lasts since we can only assign a small number of time slots per configuration.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".