Credit-Based Flow Control for Multihop Wireless Networks and Stochastic Petri Nets Analysis
Why this work is in the frame
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Bibliographic record
Abstract
As the increase of data traffic is expected to grow faster than wireless capacity, dealing with congestion is unavoidable in the future. The capacity imbalance between wired and wireless links leaves a bottleneck on the wireless link while traffic keeps coming in through the bigger pipe. In current systems, the dynamics of a wireless channel and the variations in the higher priority real time traffic both make full link utilization impossible. Either the buffers are full, giving a full link but resulting in packet losses in the queues or the link is underutilized. Closed loop credit based flow control can solve this problem by avoiding any buffer overflow completely and providing enough packets in the buffers to ramp up immediately when the channel has remaining capacity. In this paper a model using generalized stochastic Petri nets (SPN) is introduced. By structural analysis the boundedness of memory is proven while a Markov state analysis of the SPN provides numeric performance results. The studied scenario includes two wireless relayed hops, a wired backhaul and traffic class separation.
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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 | 0.000 |
| 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