A hybrid randomized initialization protocol for TDMA in single-hop wireless networks
Bibliographic record
Abstract
Although collision free TDMA schemes have been proposed and used for more than two decades, an important ingredient of these schemes, the initialization of stations (that is, assigning ID numbers 1,2,...,n) was not investigated until recently. Binary and n-ary partitioning algorithms were recently proposed for the case of stations with collision detection capability. The main contribution of this paper is a new randomized hybrid initialization protocol which combines the two partitioning algorithms into a more efficient one. The new scheme optimizes the binary partition protocol for small values of n (e.g. n=2, 3, 4). The hybrid scheme then applies n-ary partition protocol on the whole set, followed by binary partition on the stations that caused collision. We proved analytically that the expected number of time slots in the hybrid algorithm with known number of users is <2.20? n. Performance of these algorithms was also evaluated experimentally by comparing it with existing algorithms, and an improvement from e? n to approximately 2.15? n was obtained.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".