Performance Analysis of RIS-Assisted Communication With Direct Link: A New Copula Application
Bibliographic record
Abstract
Reconfigurable intelligent surfaces have received remarkable attention as promising solutions to enhance the capacity and coverage of wireless cellular networks. In this paper, we evaluate the performance of reconfigurable intelligent surfaces-assisted communication systems in the presence of phase noise, and consider a direct link in conjunction with cascade link between the transmitter and receiver by exploring different copulas. In particular, we first analyze the cumulative distribution function and the probability density function of the signal-to-noise-ratio distribution under phase noise in the presence of both direct and cascaded channel links. Moreover, we propose a copula-based solution to effectively model the non-linear dependencies among signal components induced by phase noise, and derive several exact closed-form expressions for outage probability in reconfigurable intelligent surfaces-assisted networks. In our method of analysis, we employ various copula families, including Archimedean copulas such as Farlie-Gumbel-Morgenstern, Frank, and Clayton, along with Elliptical copulas such as Gaussian and Student-T. Finally, numerical results are shown to confirm the validity of the closed-form expressions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".