An improved data flushing MAC protocol for IEEE 802.11 wireless ad hoc network
Bibliographic record
Abstract
We propose a data flushing data transfer (DFDT) protocol. The distributed coordinate function (DCF) of IEEE 802.11 supports data transmissions using the data-ACK method and the request-to-send/clear-to-send (RTS/CTS) method. The data-ACK method has a low protocol overhead, however, the transmissions are prone to collision. Although the RTS/CTS mechanism reduces the probability of collisions of data packets, the handshaking generates extensive overhead. Another issue with the IEEE 802.11 DCF is the contention for channel access; much bandwidth is wasted with the contention, especially when the mean data length is short. DFDT is capable of sending out multiple data packets from the upper layer, after acquiring channel access by a successful contention, within one frame which we call compiled MPDU (cMPDU). Right after the transmission of the data frame, the destination nodes will reply an positive/negative acknowledgement in a consecutive manner. By using this method, the protocol overhead is relatively lowered while retaining service quality and the waste of bandwidth for contention is also reduced. Simulation results show that DFDT can handle higher traffic load and has better throughput then the IEEE 802.11 MAC protocol.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".