Random Unitary Beamforming with Partial Feedback for Multi-Antenna Downlink Transmission Using Multiuser Diversity
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Bibliographic record
Abstract
Multiuser diversity is well known to require a large number of users but, in this case, the requirement of a large amount of feedback (which is proportional to the number of users) may be rather critical in limiting practical applications of multiuser diversity schemes. In this paper, we study the problem of downlink transmission in multi-antenna wireless communication systems using multiuser diversity and partial user feedback. The key idea of our approach is to reduce the amount of feedback via a random unitary beamforming and sorting the users allowed for a feedback from the total number of users by means of thresholding their normalized cross-correlation with each of the beams. To address fairness issues, an equal ratio scheduling (ERC) is proposed which is applicable to scenarios with users that may have different rate requirements. Simulation results demonstrate the performance of the proposed techniques as compared to earlier multi-antenna transmission scheduling algorithms.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it