Ris and Beamformer Optimization Using Hybrid Full-Wave Analysis in Multiuser Mimo Networks
Bibliographic record
Abstract
This paper presents an accurate modeling framework for reconfigurable intelligent surfaces (RISs) in multipleinput multiple-output (MIMO) communication systems, leveraging channel information to enhance system performance. The proposed method combines hybrid ray-tracing (RT) and fullwave analysis to derive deterministic channel responses, establishing a closed-form relationship between the communication channel matrix and the RIS's load impedances. This accurate modeling framework enables the construction of a non-diagonal impedance matrix that effectively captures critical structural factors of the RIS, such as truncation effects and mutual coupling between elements. Building on this, an optimization framework is developed to co-optimize the RIS configuration and beamforming matrix using an alternating optimization technique, with the goal of maximizing the minimum achievable rate among multiple users. A$3 \times 3$RIS-assisted system operating in an indoor environment is considered to validate the approach. The results reveal that integrating the optimized RIS significantly boosts the achievable rate for all users. Specifically, the user rates increase from approximately$1.91 \text{bps} / \text{Hz}$in the no-RIS scenario to$2.51 \text{bps} / \text{Hz}$with the optimized RIS deployed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".