Device Association and Coverage in STAR-RIS Assisted RSMA Communication Networks
Bibliographic record
Abstract
This paper proposes a device association strategy for rate-splitting multiple access (RSMA) communication aided with simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), and investigates the ensuing system coverage performance. The device association is founded on the likelihood of line-of-sight links between the devices and the base station. Modeling the distributions of the devices and the STAR-RISs as Poisson point processes, and using stochastic geometry tools, closed-form and approximate expressions for the probability of LoS are obtained, enabling the classification of the devices into two categories:direct devices, which can be served directly by the base station, andindirect devices, which are to be served with the aid of STAR-RISs. Upon this classification and the proposed device association strategy, the coverage probability of the RSMA-based communication system is derived in closed form, considering the Nakagami-$m$model for the channels' fading. Furthermore, comparisons with a NOMA-based benchmark are conducted. The numerical results validate the analytical findings and show that the proposed association strategy efficiently classifies the system's devices and leverages the STAR-RISs as needed to ensure full coverage for all devices. Finally, the impact of key system parameters on the coverage performance is analyzed, and the proposed RSMA-based approach is shown to outperform the benchmark in terms of coverage performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".