Characterizing the performance of beamforming WiFi access points
Bibliographic record
Abstract
Recently beamforming WiFi access points (APs) have been commercially available from multiple vendors. The promise of beamforming APs is the enhanced range and data transmission rate, albeit at a premium price for the AP which can be an order of magnitude more expensive than regular omnidirectional APs. In this work, through live measurements, we study the throughput performance of beamforming APs and compare it with that of regular omnidirectional APs. We consider two systems with multiple WiFi clients and: 1) a single expensive beamforming AP, and 2) multiple low-cost omnidirectional APs. We find that while in some situations the beamforming AP outperforms multiple regular APs when downloading data, in other scenarios typical of home and office use, multiple regular APs results in higher throughput and service quality. Moreover, multiple regular APs always outperforms the beamforming AP when uploading data.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".