Performance analysis of low-complexity dual-cell random beamforming transmission with user scheduling
Bibliographic record
Abstract
In this paper, we study three low-complexity random beamforming transmission schemes for dual-cell multiuser multi-input-single-output systems. Among them, selfish random beamforming and interference-aware random beamforming need no information exchange between cells, while random beamforming with limited coordination (LC-RB) selects the beamforming vector and users with the help of a small amount of overhead signaling. We develop the exact analytical expressions of the ergodic sum-rate for the resulting systems, based on which, we compare the performance of the proposed schemes in dual-cell environment. We show through selected numerical examples that LC-RB achieves tremendous performance gain over the other schemes, especially when users are populated along the cell boundary, while only requiring beam index sharing between base stations. Furthermore, we propose an adaptive implementation strategy for the more general scenario, where users are arbitrarily distributed within the cell.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".