Adaptive contention access suspension in IEEE 802.15.3 MAC
Bibliographic record
Abstract
In IEEE 802.15.3 MAC, CSMA/CA is used in contention periods (CPs) to send commands. The brief occurrences of CPs cause bursty channel access, thus conventional models based on Poisson arrivals and saturation assumptions are no longer applicable. In this paper, we model CP access in each superframe as a contention resolution problem by applying a frame aggregation strategy for efficient frame transmissions in CPs. Insight gained from this problem formulation motivates us to propose a novel Adaptive CP Suspend (ACS) scheme that is easily implemented using a CP Counter (CPC) at the piconet controller (PNC). The CPC counts down in each idle slot and resets with the appropriate contention windows size after each collision. When the CPC reaches zero, which implies the completion of the contention resolution process, the PNC can safely suspend the remaining CP and devices (DEVs) can go into SLEEP mode to save power. Simulation results show that ACS effectively adapts to changes in channel traffic, substantially shortening the effective region in a CP in which all active DEVs continue to sense the channel, and significantly reducing the system energy cost by allowing DEVs to turn off their radios during the suspended parts of the CPs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".