Robust Downlink Data Transmission in LEO Satellite–Terrestrial Networks: A Rate-Splitting Multiple Access Approach
Bibliographic record
Abstract
Rate-splitting multiple access (RSMA) has recently gained attention in low earth orbit (LEO) satellite-terrestrial networks (LSTNs), due to its ability to provide high spectral efficiency in the context of constrained energy resources of LEO satellites. However, the impracticality of acquiring perfect real-time channel state information (CSI), due to high satellite mobility and long link delay, poses significant challenges to effective utilization of RSMA in LSTNs. To tackle this challenge, we propose a location-based robust RSMA scheme for downlink data transmission in LSTNs. First, we establish an optimization problem to minimize the power consumption of LEO satellites, while meeting user requirement on the real-time data rate violation probability. Subsequently, we transfer the probability constraints of rate violation probabilities into closed-form inequalities, by utilizing Markov inequality, Jensen’s inequality, and Cauchy-Schwarz inequality. The original problem is then transformed into a Markov decision process (MDP), and a Transformer encoder-based deep reinforcement learning (TDRL) algorithm is proposed to solve the complex problem based on the real-time locations of users and the LEO satellite. Additionally, a multi time-frame location-based training dataset generation method is proposed for the training of TDRL model, considering the mobility of LEO satellite. Simulation results demonstrate that the proposed scheme is effective in guaranteeing the rate violation probability requirement of each user, and RSMA significantly outperforms space division multiple access (SDMA) and non-orthogonal multiple access (NOMA), with TDRL achieving faster convergence than other baselines.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".