Multi-Satellite Cooperative Communication: Exploiting Time Asynchrony in Non-Orthogonal Transmissions
Bibliographic record
Abstract
The ever-increasing densification of the low-Earth orbit constellation makes it possible to boost the data rate via multi-satellite cooperative transmission. However, the large spatial scale of satellite networks makes the time asynchrony non-negligible. In this paper, we exploit the asynchronous non-orthogonal transmission for multi-satellite cooperative communication to improve the fairness-aware rate. The asynchronous capacity is extended to the multi-satellite cooperation scenario and then utilized to formulate the optimization problem, which jointly considers the satellite-to-terminal association, power allocation, and decoding order. To dissect those coupled variables, we propose a preference-list-based algorithm that iterates between the following two stages. First, the many-to-many two-sided matching is solved by a Gale-Shapley algorithm based strategy given prespecified preference lists. Then, transmit power and decoding order are jointly optimized by a Dinkelbach-like algorithm. Based on the above results, the preference lists are updated to reflect the inter-satellite interference for iterative refinement. Simulation results show that introducing cooperative transmission improves the fairness rate by 12%, and exploiting time asynchrony provides another 7% gain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".