Energy Efficiency Optimization in RIS-assisted ISATRNs with RSMA: A Federated Deep Reinforcement Learning Approach
Bibliographic record
Abstract
The performance of integrated satellite-aerial-terrestrial relay networks (ISATRNs) faces two main challenges, severe signal strength degradation over long transmission distances and limited spectrum resources. To address these issues, we consider the introduction of high altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) carrying reconfigurable intelligent surface (RIS) as relays during transmission from satellites to the ground. Additionally, we employ rate splitting multiple access (RSMA) at HAPs to improve signal transmission robustness. To optimize system energy efficiency, we formulate a multi-objective problem that considers the active transmit beamforming vector, RIS phase shift, power splitting ratio, and UAV trajectory. To tackle the non-convex problem involving both discrete and continuous variables, we introduce a novel approach called access-free federated deep reinforcement learning (AF-DRL). The optimal transmit beamforming and power splitting ratio are obtained by allowing the UAV to plan its path and locally train, reducing computational overhead caused by high-dimensional UAV movement. Simulation results demonstrate that the proposed RSMA-based enhancement scheme achieves higher energy efficiency compared to the comparison scheme.
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 | 0.000 |
| 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".