Dimensioning the packet loss burstiness over wireless channels: a novel metric, its analysis and application
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Bibliographic record
Abstract
The packet loss burstiness over wireless channels is commonly acknowledged as a key impacting factor on the performance of networking protocols.An accurate evaluation of the packet loss burstiness, which reveals the characteristics and performance of the wireless channels, is crucial to the design of wireless systems and the quality-of-service provisioning to end users.In this paper, a simple yet accurate analytical framework is developed to dimension the packet loss burstiness over generic wireless channels.In specific, we first propose a novel and effective metric to characterize the packet loss burstiness, which is shown to be more compact, effective, and accurate than the metrics proposed in existing literature for the same purpose.With this metric, we then develop an analytical framework and derive the closed-form solutions of the packet loss performance, including the packet loss rate and the loss-burst/loss-gap length distributions.Lastly, as an example to show how the derived results can be applied to the design of wireless systems, we apply the analytical results to devise an adaptive packetization scheme.The proposed packetization scheme adaptively adjusts the packet length of transmissions based on the prediction of the packet loss rate and loss-burst/loss-gap lengths of the wireless channel.Via extensive simulations, we show that with the proposed packetization scheme, the channel throughput can be enhanced by more than 10% than the traditional scheme.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it