Estimating Heavy-tails in Long-range Dependent Wireless Traffic
Bibliographic record
Abstract
Wireless traffic and packet-based traffic in general possess heavy-tail marginal distributions and long-range dependence (LRD). The tail parameter /spl alpha/ of a heavy-tail distribution controls the variability of its realizations. Several traffic models, statistical test and resource management algorithms rely on the accurate estimation of the tail parameter. Conventional estimators for the tail parameter only work well when the data is short range dependent. In this paper we propose a new method to estimate the tail parameter from LRD data. This is achieved by utilizing the wavelet transform and extreme value theory. The algorithm is then applied to two stochastic processes that possess both heavy-tail marginal distributions and LRD. Results from our simulation show that the proposed method gives good estimates of /spl alpha/. We then estimate the tail parameter from a recently collected IEEE 802.11b traffic trace.
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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 | 0.000 |
| Open science | 0.001 | 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".