Multiple Access Strategy for Complex Integrated Satellite-Terrestrial Networks of Multiconstraint and Multicooperation Modes
Bibliographic record
Abstract
Integrated satellite-terrestrial networks (ISTNs) are increasingly recognized for their global communication. However, the existing research mainly focuses on simplified ISTNs, where 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. To address this gap, we investigate a more realistic and complex ISTN configuration, characterized by a large number of BSs, each divided into interference and service areas. Based on two cooperative modes, i.e., overlay and underlay spectrum sharing, two multiple access schemes are proposed for complex ISTNs using promising rate-splitting technology. These schemes consider multiple constraints simultaneously, such as communication delay, information rate, and power limit. Furthermore, a delay-rate adaptive user grouping strategy is proposed according to communication delay and information rate. For these schemes, the corresponding weighted sum rate problems are formulated, and an improved alternating optimization (AO) method is designed to solve the nonconvex challenges in two spectrum sharing modes. Moreover, a satellite-terrestrial coordinated iteration strategy based on AO is proposed to reduce the computational complexity in underlay spectrum sharing. Simulation outcomes confirm the advantages of our proposed schemes 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".