IEEE 802.11 Retry Rate Analysis with a Hybrid Artificial Intelligence Approach
Bibliographic record
Abstract
This paper aims to provide a real time analysis and prediction of the next state of a wireless network. The main idea behind this paper is to use Unscented Kalman Filter leveraging Q-learning, as an AI agent, to train the model in the presence of Co-Channel Interference (CCI) to predict clients' retry rate in a crowded network. Hence, informing the network admin in advance to take preemptive preventive actions such as changing frequency bands or powering up more Access Points. The goal is to improve wireless clients' experience in a congested network. This study contains three phases. First, making API calls and fetching the data. Second, finding possible CCI-affected clients in 5GHz frequency band. Third, analyzing their RF experience and predicting their retry rate. The mentioned phases are coded in Python using the available libraries. Initially, predictions may be inaccurate due to limited knowledge of the environment, but continuous feedback allows the model to adapt, gradually producing outputs that closely match real data.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 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".