Quasi-convex designs of max-min linear BC precoding with outage QoS constraints
Bibliographic record
Abstract
We consider a broadcast channel (BC) in which the base station is equipped with multiple antennas and each user has a single antenna. We study the design of systems with linear precoders and probabilistically-constrained quality of service (QoS) requirements for each user, in scenarios with imperfect channel state information (CSI) at the transmitter. Each user's QoS is expressed as an upper bound on the outage probability of the received signal-to-interference-plus-noise ratio. Given a total power constraint on the transmitter, we consider the design of a linear precoder so as to maximize the minimum QoS requirement of all users. We propose stochastic models for the uncertainty in the CSI of each user that are suitable for uncertainties resulting from estimation errors, and those resulting from quantization errors in systems with quantized feedback of the CSI. We formulate the design problem as a chance constrained optimization problem, and we adopt a conservative approach that yields deterministic quasi-convex formulations that are efficiently-solvable. Our simulations indicate that the proposed methods can significantly increase the minimum QoS of all users when the QoS requirements are formulated as outage constraints.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".