When URLLC is Worse than Rayleigh: Error Rates of Lomax Fading
Bibliographic record
Abstract
Ultra-Reliable Low-Latency Communication (URLLC) is a key component of 5G New Radio (NR), providing support for applications requiring highly reliable communication with latencies as low as 1 millisecond. URLCC is relevant for military communications, which demand real-time situational awareness, autonomous system support, and robust coordination. The Lomax distribution has emerged as a prime candidate for modeling URLLC channels due to its ability to capture rare but extreme fading events that are "worse" than Rayleigh fading and its good fit to empirical data. This paper presents a novel approach for deriving closed-form expressions for the symbol and bit error rates of coherently detected linear modulation, both with and without channel coding, under a class of fading distributions that include Lomax. We extend this approach to selection combining (SC), relevant for URLLC’s preemptive repeat transmissions and diversity techniques such as frequency and space diversity. Additionally, we analyze performance under double scattering, which is relevant to amplify-and-forward relaying. Our findings provide critical insights into URLLC system performance under Lomax fading, supporting the development of robust communication strategies for mission-critical military applications in harsh environments.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".