Joint Broadcast and Unicast Transmission Based on RSMA and Spectrum Sharing for Integrated Satellite–Terrestrial Network
Bibliographic record
Abstract
The integrated satellite-terrestrial network (ISTN) is gaining attention for seamless communication services, which can provide diverse services to terminals, i.e., broadcast and unicast services. However, it is challenging to address massive terminal access and meet diverse information services under limited spectrum resources and strong multiple access interference in ISTN. Dynamic spectrum sharing and rate-splitting multiple access (RSMA) have emerged as promising technologies, where RSMA offers non-orthogonal transmission and robust interference management. Motivated by this, we establish a downlink non-orthogonal broadcast and unicast (NOBU) model using the 1-layer rate-splitting strategy for ISTN, which encodes broadcast data and unicast data into common and private streams. Then, we propose four NOBU transmission schemes based on different spectrum sharing modes to maximize the max-min rate (MMR), where schemes based on hybrid spectrum sharing consider the unevenly distributed and time-varying spectrum resources and the number of terminals. Furthermore, we formulate joint MMR optimization problems while satisfying the broadcast information rate requirement in ISTN. To tackle these non-convex problems, we introduce an improved alternating optimization algorithm based on weighted minimum mean square error. Simulation results verify that the RSMA-based NOBU schemes have significant performance gains compared with various baseline schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".