Uplink Coverage of Machine-Type Communications in Ultra-Dense Networks
Bibliographic record
Abstract
Extended coverage is an essential requirement of the successful deployment of Machine-Type Communication (MTC) in harsh scenarios. This would require the MTC nodes to transmit their measurement reports with a high transmit power. At the same time, it is desirable to minimize the transmit power of the MTC nodes for the sake of a longer battery lifetime. These contradicting targets make the uplink coverage one of the main limiting factors to the fruition of MTC applications. In this paper, we analyze the uplink coverage of MTC considering the distinguishing features of a dense network including the high density of cells and the short distances between the cells and the MTC nodes. We model the path loss as a stretched exponential path-loss (SEPL) to capture the short link distances. In addition, a truncated channel inversion power control is considered to satisfy the strict requirements on the power consumption of MTC nodes. The accurate and tractable results unveiled the impact of the system parameters on the network performance. Particularly, the uplink coverage significantly improved at moderate cell density, reasonable bandwidth, and low transmission power. Interestingly, our results reveal that the maximum transmit power has no impact on the uplink coverage in the considered scenario. Fortunately, this allows for a longer lifetime for the battery-powered devices of future IoT applications.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".