Optimizing Energy Efficiency in Large RIS-Aided Multi-User MISO Communication Systems
Bibliographic record
Abstract
In this paper, we address a Multi-User Multiple-Input Single-Output (MU-MISO) communication system enhanced by a Large Reconfigurable Intelligent Surface (LRIS). The LRIS significantly improves beamforming gain and communication reliability, boosting system performance through increased signal manipulation flexibility due to its extensive number of reflecting elements. However, the deployment of a very large RIS brings challenges such as high power consumption and complexity in element management, necessitating a balance between size benefits and energy efficiency (EE). To mitigate these challenges, we propose partitioning the LRIS into smaller sub-RIS units. This approach, which involves beamforming and collaboration among multiple sub-RISs, aims to enhance scalability while maintaining EE. Unlike previous studies where all elements are continuously active, our method introduces a novel sub-RIS on-off mechanism for flexible activation and deactivation. We formulate an optimization problem to maximize total EE and use alternating optimization-based iterative algorithms, along with quadratic transform, variable substitution, and convex approximation methods, to derive sub-optimal solutions. Our analysis demonstrates that the sub-RIS architecture results in minimal performance degradation and significantly improves energy efficiency through flexible activation and deactivation of sub-RIS units.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".