Evaluating Machine Learning Techniques for Predicting Link Instability in Wireless Networks to Support Live Video Streaming
Bibliographic record
Abstract
This paper evaluates various Machine Learning (ML) techniques for classifying or predicting link instability, which we define as sudden increases in application-level packet loss and latency, or decreases in available bandwidth over a short, pre-determined period of time. To train and evaluate these ML techniques, we first construct attributes from raw wireless LTE traces. These attributes include statistical summaries (e.g., average and standard deviation) and trends (e.g., slope and correlation) of various network metrics. We then determine the attributes that provide the greatest predictive ability to construct our ML examples for training and testing. Using these ML examples, we evaluate the predictive capability of various ML techniques. We find that we can classify 5%-46% of sudden bandwidth drops, 40%-100% of increases in application-level packet loss, and 40%-100% of spikes in latency while maintaining false positive rates below 0.1. In general, we find that Decision Trees and K-Nearest Neighbours techniques provide the highest classification rate while incurring low false positive rates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".