Bibliographic record
Abstract
In this paper, we describe the design, implementation and evaluation of a new framework for the trace-based evaluation of 802.11n networks, which we call T-SIMn. We first develop novel techniques for collecting and processing traces for 802.11n networks that incorporate Frame Aggregation (FA). We then demonstrate that the simulator portion of our framework (SIMn) accurately simulates throughput for one, two and three-antenna Physical Layer Data Rates in 802.11n with FA. Finally, we evaluate the T-SIMn framework (including trace collection) by collecting traces using an iPhone which is representative of a wide variety of one antenna devices. We show that our framework can be used to accurately simulate these scenarios and we demonstrate the fidelity of SIMn by uncovering problems with our initial evaluation methodology. We expect that the T-SIMn framework will be suitable for easily and fairly comparing algorithms that must be optimized for different and varying 802.11n channel conditions which are challenging to evaluate experimentally. These include rate adaptation, frame aggregation and channel bandwidth adaptation algorithms. git
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".