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: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">direct devices</i>, which can be served directly by the base station, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">indirect devices</i>, 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-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".