Unequal Timeliness Protection Random Access Scheme for Satellite Internet of Things
Bibliographic record
Abstract
To satisfy the diversified timeliness requirements in massive machine-type communications (mMTC) for satellite Internet of Things (S-IoT), we propose two unequal timeliness protection (UT) schemes based on the grant free age-optimal (GFAO) random access protocol, where the number of access slots in a frame can be adjusted according to the system load to achieve the required age of information (AoI) performance. We first propose the independent UT protection (IUT) scheme, where the different groups of user equipments (UEs) are successively access according to their AoI priority. Then, we propose the expanded UT protection (EUT) scheme, where the lower priority groups are allowed to offloading access with the higher priority groups. By exploiting Markov analysis through tracing the instantaneous AoI evolution of UE from each priority group, we derive the closed-form expressions to the average AoI (AAoI) of different priority groups and the system AAoI for multitype services coexistence mMTC in practical S-IoT. Simulation results show that both of IUT and EUT schemes can satisfy the AAoI of the higher priority groups, and the EUT scheme can improve the AAoI of the lower priority group, thus improve the system AAoI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".