Downlink coverage and average cell load of M2M and H2H in ultra-dense networks
Bibliographic record
Abstract
In this paper, we study the impact of the coexistence of Machine-to-Machine (M2M) communication and Human-to-Human (H2H) communication on the network performance in Ultra-Dense Networks (UDNs). The performance evaluation of the network considers the downlink coverage, the average cell load of H2H users and the average uplink cell load of M2M devices. Using tools from stochastic geometry, we develop tractable expressions for the considered performance metrics. Furthermore, we investigate two association schemes to address the severe interference in UDNs, namely, connect to closest (de) base station and connect to active (CIA) base station. Considering the distinguishing features of UDNs, we model the path loss by a Stretched Exponential Path Loss (SEPL) model to address the close proximity of users to the base stations (BSs) and the high probability of Line-of-Sight (LOS) transmission as well. The simulation results show an accurate match with the analytical results. The network coverage significantly improves in C2A association scheme with no impact on the average cell load of H2H users. On the other hand, the average uplink cell load of M2M devices increases in C2A association.
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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 | 0.001 | 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 | 0.000 |
| 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 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".