Performance Analysis of ARQ Protocols using a Theorem Prover
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Bibliographic record
Abstract
Automatic-repeat-request (ARQ) protocols are widely used in modern data communications to guarantee reliable transmission over imperfect physical links. The behavior of an ARQ protocol largely depends on a number of network parameters and traditionally simulation is used for their performance analysis. However, simulation provides less accurate results and usually requires enormous amount of CPU time in order to attain reasonable estimates. To overcome these limitations, we propose to conduct the performance analysis of ARQ protocols in the environment of a higher-order-logic theorem prover (HOL). We present an approach to formally model the delay characteristics of ARQ protocols as a function of geometric random variable in higher-order-logic. In particular, we develop higher-order-logic models that describe the delay behavior of three basic types of ARQ protocols, i.e., Stop-and-Wait, Go-Back-N and Selective-Repeat. The paper also includes the verification of the average message delay relations for these three protocols in HOL.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it