Probabilistically-constrained approaches to the design of the multiple antenna downlink
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Bibliographic record
Abstract
We consider the downlink of a cellular system in which the base station is equipped with multiple antennas and each user has a single antenna. We study the design of linear precoders with probabilistically-constrained Quality of Service (QoS) requirements for each user, in scenarios with uncertain channel state information (CSI) at the transmitter. Our goal is to design the precoder so as to minimize the total transmitted power subject to the satisfaction of the QoS constraints with a maximum allowed outage probability. We consider two stochastic models for the uncertainty in the channel coefficients of each user. The first is a Gaussian model that is appropriate for uncertainty that results from estimation errors. The second one is uniform model that is appropriate for the quantization errors in systems with quantized feedback of channel state information. We formulate the design problem as a chance constrained optimization problem, in which each chance constraint involves randomly perturbed second order cone constraints. We adopt a conservative approach that yields (deterministic) convex and efficiently-solvable design formulations that guarantee the satisfaction of the probabilistic QoS constraints. Furthermore, based on these convex formulations, we propose computationally-efficient algorithms that can reduce the level of conservatism in the initial formulations. Our simulations indicate that the proposed methods can significantly expands the range of QoS requirements that can be satisfied in the presence of uncertainty in the CSI.
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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 | 0.000 |
| 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 it