Lens-Type Redirective Intelligent Surfaces for Multi-User MIMO Communication
Bibliographic record
Abstract
This paper explores the idea of usingredirectivereconfigurable intelligent surfaces (RedRIS) to overcome many of the challenges associated with the conventionalreflectiveRIS. We develop a framework for jointly optimizing the switching matrix of the lens-type RedRIS ports along with the active precoding matrix at the base station (BS) and the receive scaling factor. A joint non-convex optimization problem is formulated under the minimum mean-square error (MMSE) criterion with the aim to maximize the spectral efficiency of each user. In the single-cell scenario, the optimum active precoding matrix at the multi-antenna BS and the receive scaling factor are found in closed-form by applying Lagrange optimization, while the optimal switching matrix of the lens-type RedRIS is obtained by means of a newly developed alternating optimization algorithm. We then extend the framework to the multi-cell scenario with single-antenna base stations that are aided by the same lens-type RedRIS. We further present two methods for reducing the number of effective connections of the RedRIS ports that result in appreciable overhead savings while enhancing the robustness of the system. The proposed RedRIS-based schemes are gauged against conventional reflective RIS-aided systems under both perfect and imperfect channel state information (CSI). The simulation results show the superiority of the proposed schemes in terms of overall throughput while incurring much less control overhead.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".