Exact Sum-Rate Analysis of MIMO Broadcast Channels with Random Unitary Beamforming Based on Quantized SINR Feedback
Bibliographic record
Abstract
There is a continuing interest in low-complexity multiuser MIMO transmission techniques based on random unitary beamforming (RUB). Different RUB schemes mandate different amount of feedback to exploit the multiuser diversity gain for sum-rate capacity benefits. In this paper, we investigate the tradeoff of sum-rate capacity versus feedback load between different RUB schemes through accurate statistical analysis. Specifically, we derive the exact sum-rate expressions for two RUB schemes based on quantized signal-to-interference-plus- noise ratio (SINR) feedback. These analysis are accompanied by the development of the complete statistical characterizations of ordered SINRs for a user, which can find their application in many other related problems. We show through selected numerical examples that the additional feedback of the quantized values of best beam SINR from each user can help considerably improve the sum-rate performance of RUB systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".