On the spatial quantization noise requirements for accurate RF coverage validation and prediction
Bibliographic record
Abstract
With the trend of cellular providers shifting to higher frequencies, there is an increasing migration to smaller cells that is further driven by the growing demand for wireless service. This obviously calls for higher resolution radio frequency (RF) validation and prediction. Yet, to the author's knowledge, there has been no study as to what resolution is required for accurate RF modeling and prediction. Many of today's computer prediction tools can provide estimates of RF signal strength at arbitrary spatial resolution. However, the choice of this resolution is often left up to the discretion of the user. Even worse, sometimes the prediction resolution is hard-coded to be the same as that of the terrain data base. Choosing a resolution bin size that is too small is both computationally inefficient and unnecessarily wasteful of valuable memory resources. Choosing a resolution bin size that is too coarse introduces ubiquitous uncertainty about the quality of RF coverage. This paper investigates the spatial quantization noise requirements of RF prediction and RF coverage validation. It is found that the minimum resolution bin size required to mitigate spatial quantization noise effects is about one fortieth of the cell radius.
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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".