Tractable approaches to fair QoS broadcast precoding under channel uncertainty
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Bibliographic record
Abstract
We consider the design of linear precoders for broadcast channels with quality of service (QoS) constraints for each user, in scenarios with uncertain channel state information at the transmitter. Given a total power constraint on the transmission power, our goal is to design a robust fair precoder that maximizes the minimum QoS over all users that can be guaranteed for every channel within a specified uncertainty region around the estimate of each user's channel. Since this problem is not known to be computationally tractable, we will derive three conservative design approaches that yield quasi-convex and computationally-efficient restrictions of the original design problem. The three approaches yield formulations that offer different trade-offs between the degree of conservatism and the size of the design problem. Our simulations indicate that the proposed approaches can significantly increase the minimum QoS of all users when the available channel knowledge at the transmitter is imperfect.
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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