Lumped Markovian Estimation for Wi-Fi Channel Utilization Prediction
Bibliographic record
Abstract
We present a model to predict the short-term utilization of an IEEE 802.11 channel. We approximate the time-varying utilization process via a Markovian state transition model and subsequently create a lumped representation of the transition matrix. Each lumped state can then be treated as a class. The lumped matrix provides a simpler to understand description of the channel utilization behavior and naturally includes the persistence in one lumped state which resembles the characteristic behavior of naive predictors (where predicted state equals the current state). We demonstrate that treating the lumped states as classes allows good prediction models to be built using Logistic Regression and Neural Network models. Our results are based on IEEE 802.11 wireless utilization data collected as reported in the channel utilization (CU) field of the QBSS Load Element in Beacon frames. The presented approach can be implemented as an edge computing task, whereby edge nodes calculate the lumped states and train models, informing nearby client devices of the model parameters, allowing them to produce predictions on their own.
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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 | 0.001 |
| 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 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".