Probabilistic Analysis of Wireless Systems Using Theorem Proving
Why this work is in the frame
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Bibliographic record
Abstract
Probabilistic techniques play a major role in the design and analysis of wireless systems as they contain a significant amount of random or unpredictable components. Traditionally, computer simulation techniques are used to perform probabilistic analysis of wireless systems but they provide inaccurate results and usually require enormous amount of CPU time in order to attain reasonable estimates. To overcome these limitations, we propose to use a higher-order-logic theorem prover (HOL) for the analysis of wireless systems. The paper presents a concise description of the formal foundations required to conduct the analysis of a wireless system in a theorem prover, such as the higher-order-logic modeling of random variables and the verification of their corresponding probabilistic and statistical properties in a theorem prover. In order to illustrate the utilization and effectiveness of the proposed idea for handling real-world wireless system analysis problems, we present an analysis of the automated repeat request (ARQ) mechanism at the logic link control (LLC) layer of the General Packet Radio Service (GPRS), which is a packet oriented mobile data service available to the users of Global System for Mobile Communications (GSM).
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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