Hierarchical Bayesian Modelling for Wireless Cellular Networks
Bibliographic record
Abstract
With the recent advances in wireless technologies, base stations are becoming more sophisticated. Network operators are also able to collect more data to improve network performance and user experience. In this paper we concentrate on modeling performance of wireless cells using hierarchical Bayesian modeling framework. This framework provides a principled way to navigate the space between the option of creating one model to represent all cells in a network and the option of creating separate models at each cell. The former option ignores the variations between cells (complete pooling) whereas the latter is overly noisy and ignores the common patterns in cells (no pooling). Hierarchical Bayesian modeling strikes a trade-off between these two extreme cases and enables us to do partial pooling of the data from all cells. This is done by estimating a parametric population distribution and assuming that each cell is a sample from this distribution. Because this model is fully Bayesian, it provides uncertainty intervals around each estimated parameter which can be used by network operators making network management decisions. We examine the performance of this method on a synthetic dataset and a real dataset collected from a cellular network.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".