Age-Oriented Transmission Protocol Design in Space-Air-Ground Integrated Networks
Bibliographic record
Abstract
In this paper, we study the age-oriented hybrid automatic repeat request (HARQ) protocol design in space-air-ground integrated networks (SAGINs) scenarios. A real-time communication system, where the updates are delivered from the remote nodes to terrestrial devices, is formulated. As the end-to-end latency$D$is nontrivial, the traditional HARQ with frequent feedbacks is not always beneficial to timely transmission. Intuitively, there is a threshold$D^{*}$of$D$, only within which retransmission is advantageous to age. Inspired by this, we formulate an age-optimal redundancy allocation problem and derive the explicit expression of$D^{*}$for advantageous retransmissions. Besides, to further increase the timeliness of the system, we propose a fast incremental redundancy hybrid ARQ protocol (fast IR-HARQ), where successive decoding and feedback operations are omitted based on channel estimation. Considering the shadowed Rician fading channel and finite blocklength regime, we derive expressions of the average age for the standard IR-HARQ and fast IR-HARQ setups. As expected, the proposed fast IR-HARQ scheme reduces the average age significantly compared with the IR-HARQ strategy. Further, we evaluate the influence of different parameters on the age performance of the fast IR-HARQ scheme. The results demonstrate the superiority of the proposed fast IR-HARQ protocol without loss of reliability.
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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 | 0.004 | 0.006 |
| 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 | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".