A geometrical probability-based approach towards the analysis of uplink inter-cell interference
Bibliographic record
Abstract
Interference signal-to-interference ratio (SIR), and other performance metrics in cellular networks depend on the distances between the nodes. As a result, a geometrical probability-based approach can shed light on the analysis of the interference and SIR. In cellular networks, hexagon geometry is considered as the preferred cell shape, as it provides compactness and coverage efficiency. We propose an analytical model for the interference and SIR using the existing knowledge of random distances related to the hexagon geometry. We derive the closed-form expressions for the probability distribution function (PDF) of the interference from one interferer, as well as the PDF of the received signal power of the intended transmission using a geometrical probability-based approach. Moreover, the distributions of the total interference power and SIR are presented in this work. Finally, the analytical results are verified through extensive simulations, which shows the accuracy of our model.
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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 | 0.002 |
| 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".