A Simple and Efficient User-Scheduling Strategy for RUB-Based Multiuser MIMO Systems and Its Sum-Rate Analysis
Bibliographic record
Abstract
We present a user-scheduling scheme for multiuser multiple-input-multiple-output (MIMO) systems with random unitary beamforming (RUB) in this paper. The new scheme, which is termed as adaptive beam activation based on the conditional best beam index feedback (ABA-CBBI), requires low average feedback load by imposing a feedback threshold on the users' signal-to-interference-plus-noise ratio (SINR) and suffers less multiuser interference by only activating those beams requested by at least one user. We derive the exact analytical expression for the sum-rate capacity of the resulting multiuser MIMO systems, based on which we examine the optimal selection of the feedback threshold in terms of sum-rate maximization. We demonstrate through selected numerical examples that the proposed ABA-CBBI scheme with optimal thresholds can achieve better sum-rate performance than existing schemes over high-signal-to-noise-ratio (SNR) regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".