Estimating End-User Throughput Using Service Provider Cell Traces Via Gradient Boosting
Bibliographic record
Abstract
The rapid adoption of 5G networks has enabled supporting applications that require high bandwidths and low latencies. Service providers need to manage their resources efficiently to avoid service interruptions and support high-quality services to their customers. Therefore, being aware of the customer's experienced bandwidth is of paramount importance. Utilizing the traces collected on the service provider's side to estimate the experienced bandwidth on the user's side is a problem that was not studied in the literature. Moreover, the traces collected by the service provider are usually sparse and missing a large number of values to be reliable in predicting the user's experienced bandwidth. In this thesis, we focus on accurately imputing missing values in the collected traces, and consequently, we build a Regularized Gradient Boosting model to predict the user's throughput using traces that are exclusively collected from the service provider's resources. Our approach shows that using our imputing and prediction approaches, we can accurately estimate the user equipment's throughput.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".