Random Unitary Beamforming With Partial Feedback for MISO Downlink Transmission Using Multiuser Diversity
Bibliographic record
Abstract
In this paper, we study the problem of downlink transmission in multiple-input-single-output (MISO) wireless communication systems using multiuser diversity. There are antennas at the base station (BS) and single-antenna receivers. The performance of multiuser diversity depends on the number of users that have independent channel realizations. It is well known that increasing the number of users improves the performance at the expense of increasing feedback that is proportional to the number of users. For sufficiently large K, the capacity scales like M log log K. When K is large, increasing feedback limits practical applications of the multiuser diversity. Our approach is to reduce the feedback by selecting a threshold for the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> -norm of the normalized cross-correlation (||z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> || <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> ) between the users' channel and the beamforming matrix based on the random unitary beamforming. The average amount of feedback per time slot is real numbers and integers which does not change with K. To improve fairness, an equal ratio scheduling algorithm which could serve the users with different rate requirements is developed. Monte Carlo simulation results is provided to verify the performance of the proposed algorithm.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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 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".