Physical Layer Measurements for an 802.11 Wireless Mesh Network Testbed
Bibliographic record
Abstract
Physical layer measurements for an infrastructure 802.11 Multichannel MultiBand Wireless Mesh Network testbed are described. Each wireless router node design consists of a Linux processor with multiple 802.11b/g transceivers operating in the 5 GHz band for backhauling, and multiple 802.11 transceivers in the 2.4 GHz band for end-user service. Each transceiver consists of a MAC and base-band processor (BBP) in addition to a radio. A Linux-based device driver has been modified to adjust the physical layer parameters. The 802.11 standard specifies three orthogonal channels, 1, 6, and 11. The routers can be programmed to implement any static mesh binary tree topology by assigning orthogonal frequency-division multiplexing (OFDM) channels to network edges. The routers can be programmed to implement any general mesh communication topology by using a time division multiple access (TDMA) frame schedule, and assigning OFDM channels to network edges within each TDMA frame. Preliminary measurements of co-channel interference and the signal to interference and noise (SINR) ratio for the network testbed are presented, using omni-directional antenna and the 802.11b operation mode. This data can be used to optimize the performance of large infrastructure Wireless Mesh networks using 802.11 technology. Index Terms—wireless mesh network; 802.11; co-channel in- terference; noise; SINR;
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".