Uplink Coverage and Capacity Analysis of mMTC in Ultra-Dense Networks
Bibliographic record
Abstract
In this paper, we investigate the uplink coverage and ergodic capacity of massive Machine-Type Communication (mMTC) considering an Ultra-Dense Network (UDN) environment. In MTC, devices equipped with sensing, computation, and communication capabilities connect to the Internet providing what is known as Internet-of-Things (IoT). A dense network would provide an all-in-one solution where scalable connectivity, high capacity, and uniform deep coverage are byproducts. To account for short link distances, the path loss is modeled as stretched exponential path loss (SEPL). Moreover, the fading is modeled as a general (α - μ) channel, where tractable and insightful results are derived for the Rayleigh fading special case. We consider the direct MTC access mode where mMTC nodes connect directly to the small cell. The analytical results disclose the impact of the system parameters and propagation environment parameters on the network performance. In particular, our results reveal that significant coverage enhancements and high uplink capacity are achievable at moderate cell densities, low transmission power, and moderate bandwidth. Moreover, the uplink network performance is independent of the maximum transmission power in the considered dense network scenario, allowing for longer battery lifetime of future IoT devices. The accuracy of the derived analytical results is assessed via extensive simulations.
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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 | 0.001 | 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 | 0.001 |
| 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".