Multiplatform Spectrum Sensing Prototype
Bibliographic record
Abstract
Smart spectrum sharing initiatives have been stimulated by the spectrum scarcity experienced nowadays. The option that secondary users may access idle channels avoiding harmful interferences to primary users is a viable option to alleviate the spectrum crunch. In this line, it is necessary to identify, in a reliable way, the real status of spectrum bands and know if the selected frequencies are used or not by the incumbent. We propose a multiplatform spectrum sensing prototype, which is capable to sense the UHF TV frequency bands from 500 MHz to 700 MHz (Channels 19 to 51) and identify the characteristic features of the primary users. Combining a high accurate spectrum analyzer, an open source RF Explorer and an RTL SDR receptor we obtain information of the TV spectrum and know its usability. Analyzing the information collected by three above mentioned devices, it is possible to identify the TV white spaces in our interest frequencies and learn about the parameters to be sensed with a low-end device with high accuracy. Results obtained shows the active channels and the availability of TV white spaces in the City of Windsor.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".