System Level Simulation Method and Performance of RIS-Assisted Wireless Networks
Bibliographic record
Abstract
Reconfigurable Intelligent Surface (RIS) is a promising technique in the next generation of wireless communication systems. Many researches have discussed the signal quality promotion introduced by RIS via link-level simulations or field tests, but few are done in the system level. In this paper we conduct a System-Level Simulation (SLS) of the RIS-assisted network based on the Geometry-based Stochastic Channel Model (GSCM), and use the results to answer two questions: 1) How much coverage gain is brought by RIS, and how many users benefit from RIS? 2) How to avoid prohibitively high complexity in the RIS SLS due to the enormous increase of RIS-related links and equivalent channels? For the first question, we derive an closed-form Reference Signal Received Power (RSRP) formula for RIS equivalent channel considering the impact of spatial directional beamforming. From the formula, we further analyze the typical propagation conditions that a user may benefit from RIS for coverage enhancement. For the second question, the RSRP distributions are derived for both the desired signal from associated BS and RIS and the adverse signal from interfering BS or RIS, from which we prove that the cross-sector RIS interference are weak enough to be neglected in the SLS, reducing the simulation complexity significantly,
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".