On the Performance of Large-Scale Wireless Networks in the Finite\n Block-Length Regime
Bibliographic record
Abstract
Ultra-Reliable Low-Latency Communications have stringent delay constraints,\nand hence use codes with small block length (short codewords). In these cases,\nclassical models that provide good approximations to systems with infinitely\nlong codewords become imprecise. To remedy this, in this paper, an average\ncoding rate expression is derived for a large scale network with short\ncodewords using stochastic geometry and the theory of coding in the finite\nblocklength regime. The average coding rate and upper and lower bounds on the\noutage probability of the large-scale network are derived, and a tight\napproximation of the outage probability is presented. Then, simulations are\npresented to study the effect of network parameters on the average coding rate\nand the outage probability of the network, which demonstrate that results in\nthe literature derived for the infinite blocklength regime overestimate the\nnetwork performance, whereas the results in this paper provide a more realistic\nperformance evaluation.\n
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| 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".