Interference measurements in an 802.11n Wireless Mesh Network testbed
Bibliographic record
Abstract
Interference measurements in an infrastructure 802.11n Wireless Mesh Network (WMN) testbed are described. Each wireless router consists of a Linux processor with multiple dual-band 802.11a/b/g/n transceivers. The 5 GHz band can be used for backhauling, and the 2.4 GHz band can be used for end-user service. The backhaul links use sectorized 3×3 MIMO directional antenna, to support directional parallel transmission over orthogonal channels. A Linux-based device driver has been modified to adjust the physical layer parameters. Each 802.11n transceiver can be programmed to transmit over a 20 MHz spectrum without channel bonding, or a 40 MHz spectrum with channel bonding. The 802.11n standard supports up to 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 time-slot. Measurements of co-channel interference, the Signal to Interference and Noise (SINR) ratio and TCP/UDP throughput for the 802.11n network testbed are presented. It is shown that maximizing TCP/UDP throughput in 802.11n networks can be challenging, even with very high SINR (30–40 dB) links, MIMO directional antenna, and frame aggregation with block acknowledgements. In order to maximize bandwidth efficiency, the highest quality (and cost) MIMO directional antenna appear to be necessary, and it is unlikely that mobile users can use such antennas. Our interference measurements can be used to optimize the performance of large WMNs using 802.11n technology.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".