A Markovian jump congestion control strategy for mobile ad-hoc networks with differentiated services traffic
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Bibliographic record
Abstract
Available congestion control schemes such as the transport control protocol (TCP) when applied to wireless mobile networks will result in a large number of packet drop outs, unfair scenarios and low throughputs due to the changing number of neighboring nodes and the unpredictable network load. In this paper, a novel Markovian jump model is presented to model the changes in the number of neighboring nodes and subsequently a Markovian Jump Congestion Control (MJCC) strategy is proposed for mobile ad hoc networks. The MJCC strategy does take into account the associated physical network resource limitations and is shown to be robust to the existing unknown and time-varying network delays. Furthermore, the MJCC controller is developed on the basis of Differentiated Services (Diff-Serv) architecture by utilizing a robust adaptive technique. A Linear Matrix Inequality (LMI) condition is obtained to guarantee the stochastic stability of the closed-loop system. Simulations results and analysis illustrate the effectiveness and capabilities of our proposed MJCC strategy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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