A Novel Approach for Performance Analysis of IoT Enabled Uplink Network with Matérn Cluster Process
Bibliographic record
Abstract
In this paper, we focus on analyzing the performance measure of cellular network for massive connectivity of IoT devices considering that the base stations (BSs) are distributed according to Mat\'{e}rn Cluster Process (MCP) while devices are distributed according to Poisson Point Process (PPP). In particular, we develop a generalized approach to calculate connection failure probability of IoT devices in the random access channel (RACH) phase of uplink (UL) transmission. The proposed approach uses a calculation of the devices' association probability rather than using an approximation of the Voronoi tessellation's cells area distribution that is used in PPP. To adopt this approach, we derive the void probability for MCP which is defined as the probability of having no children point of MCP in a given distance. The merit of the proposed approach besides its exact analysis, is that it can be applied for general scenarios of devices and BSs' distributions. Presented numerical results demonstrate the effectiveness and accuracy of the proposed approach. We validate our analysis comparing with the numerical simulations via MATLAB.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".