Robust MISO downlink: An efficient algorithm for improved beamforming directions
Bibliographic record
Abstract
The design of a set of beamformers for the multiple-input single-output (MISO) downlink that provides the scheduled users with the quality-of-service (QoS) level that they requested can be quite sensitive to the accuracy of the channel state information (CSI) that is available at the base station. To mitigate that sensitivity, models for the uncertainty in the CSI can be incorporated in the design formulation, but the resulting optimization problems are typically difficult to solve. A recently developed low-complexity algorithm for the power loading problem, in which the beamforming directions are pre-defined, has demonstrated good performance in numerical experiments. In this paper the reasons that underlie that good performance are examined. Then, using insights from that analysis, a computationally-efficient algorithm for jointly designing the beamforming directions and the power loading is developed. The resulting beamformers provide a significant reduction in the outage probability, and a hybrid of the two algorithms provides even further reduction.
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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".