On Modeling Contention-Based MAC Protocols Using Markov Chains
Bibliographic record
Abstract
Using discrete-time Markov chains to analyze the performance of contention-based medium access control (MAC) protocols has been a common practice since its initial proposal by Bianchi in his seminal paper published in 2000. The way these methods work is by modeling each individual node competing for the medium in a wireless network as a Markov chain. Performance metrics of the whole system, such as throughput, delays and losses, can then be obtained from the model. However, these methods implicitly assume that Markov chains modeling individual nodes are independent from each other, which is not necessarily true in most situations. In this work we propose a more realistic model in which the whole network with all of its nodes is modeled using a single Markov chain, and in which assuming independence is no longer necessary. We reduce the complexity of the problem, both in processing and memory requirements, by taking advantage of its inherent symmetry, which allows us to work with only a few selected representative states. We evaluate how results from both types of methods compare to each other and to simulations. Our findings show that the old method has a very poor performance as compared to the new one.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".