A Limited-Feedback Scheduling and Beamforming Scheme for Multi-User Multi-Antenna Systems
Bibliographic record
Abstract
This paper proposes an efficient two-stage limited feedback beamforming and scheduling scheme for multiple antenna cellular communication systems. The system model includes a base-station with M antennas and a large pool of users with a total feedback rate of B bits per fading block. The feedback process is divided into two stages. In the first stage, the users measure their channel gains from each antenna and feedback the index of the antenna with the highest channel gain along with the gain itself. Based on this information, the base station schedules M users with the highest channel gains from its M antennas and polls those users for explicit quantization of their vector channels in the second stage. Based on these quantized channels, the base-station then forms zero-forcing beamforming vectors for downlink transmission. This paper presents an approximate analysis for the proposed scheme which is used to optimize the bit allocation between the two feedback stages. It is shown that for a total number of feedback bits B, the number of feedback bits assigned to the second stage, B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , should scale as M(M-1) log(SNR × B). In particular, the fraction B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /B behaves as logB/B in the asymptotic regime where B → ∞. Further, the approximate downlink sum rate is shown to scale as M log SNR + M log log B, suggesting that both multiuser multiplexing and multiuser diversity gains are realized. As the numerical results verify, the proposed feedback scheme, in spite of its low complexity, performs very close to the more complicated beamforming and scheduling schemes in the literature and in fact outperforms such schemes in the high-SNR regime.
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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 | 0.000 | 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".