A Novel ASAPPP Approach to Characterize the SIR Distribution in General Cellular Networks
Bibliographic record
Abstract
The non-Poisson point processes have the characteristics of spatial repulsion or aggregation, which well matches the realistic cellular network deployment, but challenges the fundamental analysis of the signal-to-interference ratio (SIR) distribution. In this letter, we propose a novel approximation method via decomposing the ASAPPP method (approximate SIR analysis based on the Poisson point process) to formulate two key intermediate approximate results, namely, the contact distance distribution and the spatial distribution of the interfering nodes. With the aid of these results, the novel ASAPPP is proved to be equivalent with the original one in a single-tier network and obtains the simple yet effective approximate results of association probability and coverage probability in a multi-tier network. Simulation results demonstrate that the proposed method provides a better approximation compared with the original ASAPPP, especially in multi-tier network scenarios.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".