Transmission Rate Analysis for Large Scale Uplink Networks in the Finite Block-Length Regime
Bibliographic record
Abstract
The development of delay-constrained applications which require high data rates, ultra-low latency, and high reliability pushed future communication technologies towards using short codes. This necessitates studying the performance of large-scale networks under short codes. Works in the literature that study large-scale uplink (UL) networks rely on the classical Shannon coding theory which assumes long codes and vanishing frame error probability, which is imprecise for short codes. Thus, this paper studies the performance of a large-scale UL network in the finite blocklength regime (FBR), under two power control schemes to mitigate the effect of interference: truncated channel inversion power control and channel inversion power control with maximum power transmission. The average coding rate, outage probability, and reliability of this network are derived in the FBR. Numerical results study the effect of network parameters and also show that the classical coding theory overestimates the average coding rate and imprecisely characterizes the outage probability and the reliability in the FBR.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".