Conducting Repeatable Experiments and Fair Comparisons using 802.11n MIMO Networks
Bibliographic record
Abstract
A commonly used technique for evaluating and comparing the performance of systems using 802.11 (WiFi) networks is to conduct experiments. This approach is appealing and important because it inherently captures critical properties of wireless signal transmission that are difficult to analytically model and simulate. Unfortunately, obtaining consistent and statistically meaningful empirical results using 802.11 networks, even in well-controlled environments, can be quite challenging and time consuming because channel conditions can vary over time. In this paper, we use 2.4 and 5 GHz 802.11n MIMO networks to study different methodologies that could be used to evaluate and compare the performance of different alternatives used in 802.11 systems (e.g., different systems, configurations or algorithms). We first illustrate that some of the more commonly used methods in existing research are flawed and explain why. We then describe a methodology called multiple interleaved trials that, to our knowledge, has not been used for, or studied on, 802.11 networks. We evaluate this methodology and find that it can be used to repeat experiments and to compare the performance of different alternatives. Finally, we discuss other possible applications of this approach for comparative performance evaluations.
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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 | 0.165 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".