Impact of the number of sensors on the network cost and accuracy of the radio environment map
Bibliographic record
Abstract
Spectrum management using a radio environment map (REM) to dynamically allocate frequencies may facilitate the efficient exploitation of the spectrum in a heterogeneous environment. The overhead costs of networking radios to create the map must be balanced against the impact of a poor REM due to low quality or quantity of sensor data. To study this cost-benefit balance, simulations were performed to evaluate a REM using a variable number of randomly-located sensors. It was seen that too few sensors provides insufficient coverage of the area of interest and may not provide connectivity to the data collection centre, whereas in a more dense sensor environment, sensor location was the most important factor. The results show that information about the REM's accuracy is required to mitigate false confidence and that exact replication of the ground truth is not required for dynamic spectrum management; rather, it is sufficient to identify the regions where spectrum could be reused.
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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 | 0.000 | 0.000 |
| 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 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".