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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3 \times 3$</tex> 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.91 \text{bps} / \text{Hz}$</tex> in the no-RIS scenario to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.51 \text{bps} / \text{Hz}$</tex> with the optimized RIS deployed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".