Realistic Cooperative Strategies Based on Dynamic Spectrum Sharing for Integrated Satellite-Terrestrial Networks
Bibliographic record
Abstract
Integrated satellite-terrestrial networks (ISTNs) are increasingly recognized for their global communication. However, much of the existing research is focused on simplified ISTNs, where the cooperative strategies between satellites and base stations (BSs) are not easily applicable to real-world scenarios. There is a pressing need to investigate more realistic and complex ISTNs to address this gap. In this context, a distributed BS strategy for scenarios with uniformly distributed terminals and a centralized BS strategy for scenarios with unevenly distributed terminals are proposed. Additionally, these BS strategies are combined with three distinct spectrum sharing modes for enhancing access flexibility. Due to supporting non-orthogonal transmissions and enhancing interference management capabilities, rate-splitting multiple access is adopted to efficiently serve numerous terminals using finite communication resources. For these strategies, the corresponding max-min rate optimization problems are developed, and then an alternating optimization (AO) method is introduced utilizing weighted minimum mean square error to resolve the non-convex challenges in overlay spectrum sharing. Moreover, an adaptive power control method, leveraging the AO algorithm, is designed to navigate the non-convex challenges in underlay spectrum sharing. Simulation outcomes confirm that the proposed schemes yield considerable performance enhancements compared to various standard schemes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".