Machine-Type Communications in mmWave Ultra-Dense Networks: Performance Analysis
Bibliographic record
Abstract
To cope with the unprecedented ubiquity of smart applications, Machine-Type Communication (MTC), the cellular communication backbone of the Internet of Things (IoT), has become an inevitable choice. In this paper, we investigate the achievable performance of MTC in an Ultra-Dense Network (UDN). To fully utilize the available resources in 5G and beyond networks, we exploit the propagation characteristics and excess bandwidth of the Millimeter wave (mmWave) band. Using tools from stochastic geometry, we provide a mathematical framework to evaluate the achievable Signal-to-Interference plus Noise Ratio (SINR) per user and the average capacity per Small Cell (SC) while considering the severe Inter-Cell Interference (ICI) of UDNs and the blockage effect in mmWave. The accuracy of the formu-lated analytical expressions is verified through extensive Monte-Carlo simulations. The obtained results show the existence of an optimal Small Cell (SC) density that maximizes the utilization of the deployed SCs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".