Distributed sensing of spectrum occupancy and interference in outdoor 2.4 GHz Wi-Fi networks
Bibliographic record
Abstract
A spectrum monitoring campaign was launched in an outdoor urban radio environment to investigate the potential deployment of Cognitive Radio (CR) Wi-Fi networks in the 2.4 GHz ISM band. The campaign used a CR learning platform with 4, 8, and 16 sensors. This paper presents Wi-Fi spectrum occupancy and interference behaviour based on the outcome of one of the measurements using 16 GPS-synchronized sensors. At detection thresholds Td≥ -62dBm, long-term spectrum holes were dominant on all Wi-Fi channels. However, at Tdrdof management packets were beacons, and more than 1/2 of data packets were data-reserved and null (no data). We also noted, at Td≤ -82dBm, a large number of Wi-Fi users more than can be attributed to the immediate surroundings of sensors, but a small set of them was dominant producing the bulk of spectrum occupancy and interference. In addition, at least 25% of these users were detected only once over the 5.5 hours measurement time span. The measurements showed spatial variations of the Wi-Fi environment over a small sensing area (21m-by-45m). We noted considerable non-homogeneity in the distribution of Wi-Fi interference at Td≥ -62dBm, but some non-homogeneity at Td≤ -82dBm. We also noted significant correlated fluctuations of received signal strength and non-reciprocal links over short distances between sensors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".