RCP throughput modeling and performance improvement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a new Markov model to analyze the throughput of the RCP (Reception Control Protocol) [1]. The model combines the service time of the 802.11b MAC layer protocol and the transmission characteristic of the RCP congestion window. Furthermore, in order to improve the throughput of WLAN (Wireless LAN) we introduce a mechanism called DREQs (Delayed REQs) in the RCP. Although this mechanism is similar to delayed ACK of TCP, it has higher robustness in wireless networks. We have extended our analysis to the throughput of DREQ-REP and proved the DREQs mechanism can improve the performance of RCP. The simulation results not only validate our throughput model but demonstrate that the DREQs can effectively improve the performance of RCP in WLAN.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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 it