Opportunistic Scheduling With Beam Focusing in Distributed RIS-Aided MU-MIMO Systems
Bibliographic record
Abstract
This paper introduces a novel cooperative framework for distributed Reconfigurable Intelligent Surfaces (RISs) in MU-MIMO systems, aimed at enhancing adaptability and performance. Multiple RISs are strategically deployed to ensure Line-of-Sight (LoS) connectivity with the Base Station (BS) and among each other, thereby improving coverage in traditionally weak zones. Each RIS can dynamically switch between acting as a main RIS (mRIS) to directly serve users or as an intermediate RIS (iRIS) to relay signals to another mRIS. A key contribution of this work is the optimization of mRIS phase shifts using a statistical RIS response function to enhance signal strength. Additionally, iRIS configurations utilize a beam focusing codebook for efficient signal transmission. An opportunistic scheduling scheme based on Lyapunov optimization adapts to user distribution and network conditions to optimize the use of the resulting spatial multiplexing gains, ensuring fairness and stability. Two scheduling policies are proposed: Slot-Based RIS Operational Mode Assignment (SB-ROMA) for high adaptability and Coherence-Time RIS Operational Mode Assignment (CT-ROMA) for reduced computational overhead. Analytical and simulation results validate the effectiveness of the proposed designs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| 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".