Improving CSMA/CA network performance under hidden collision
Bibliographic record
Abstract
Abstract Dynamic spectrum access policy allows a secondary user (SU) to access a primary user (PU) channel when it is idle. However, the idle state may result from the PU's silent activities, which can be wrongly perceived as an opportunity for the SU to access the channel and lead to “hidden collision” when it effectively tries to access the channel under this condition. At best of our knowledge, this issue has yet to be addressed in the literature. In this paper, we will first define a three‐state model that describes hidden collision conditions, then propose a probabilistic model in which a transient state is added to force the SU to wait a certain period of time before accessing the channel, thereby translating into better protection for the PU. Based on this model and using Carrier Sense Multiple Access/Collision Avoidance protocol, we will evaluate the PU's and SU's throughput and delay with and without transient state as well as the gain in using our approach. Our computation results show a substantial improvement of the PU's throughput from 19.6 to 61.1%.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".