Random Access with and without Sensing in Non-Terrestrial Networks for Timely Updates
Bibliographic record
Abstract
The growing boom in time-critical applications such as remote sensing and monitoring has made low latency of information an important requirement. Age of information (AoI) has been proposed to measure the freshness of information from the receiver side. In this paper, we analyze that multiple sources transmit their status packets to a remote controller for timely updates. Characterized by long transmission distances, satellite networks are commonly using Aloha as a random access protocol by preconceiving channel sensing is low efficient. Yet, for some non-terrestrial networks where the propagation delay is comparable to the transmission time, the performance comparison between Aloha and CSMA requires more detailed consideration. By building the node-centric discrete-time Markov chain, we quantify the performance of Aloha and CSMA on AoI and give the performance break-even point. Only when the ratio of propagation delay to transmission time is larger than this point, Aloha performs better on the timeliness metric. Furthermore, we derive the optimal attempt probability of CSMA to achieve the lowest latency. In the end, simulation results confirmed the validity of the theoretical analysis.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".