Statistically Robust Transceiver Design for Broadcast Channels with Uncertainty
Bibliographic record
Abstract
We consider the design of linear transceivers for the downlink of a multiuser communication system in the presence of uncertain channel state information (CSI) at the base station. We focus on systems in which the base station has multiple antennas and each user has a single antenna, and we consider a stochastic model for the uncertainty in the CSI. We study the joint design of a linear precoding matrix at the base station and the equalizing gains at the receivers so as to minimize the average, over channel uncertainty, of the total mean-square-error (MSE). By generalizing the MSE duality between the broadcast channel (BC) and multiple access channel (MAC) to scenarios with uncertain CSI, we obtain a relation between the desired robust broadcast transceivers and the corresponding transceivers that optimize the same performance metric for the dual multiple access channel. Furthermore, we present a convex optimization formulation for the robust transceivers of the dual MAC that can be efficiently solved. Our simulations indicate that the proposed approach can significantly reduce the sensitivity of the downlink to uncertainty in the CSI, and can provide improved performance over that of existing robust designs.
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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".