Design of Fair Multi-user Transceivers with QoS and Imperfect CSI
Bibliographic record
Abstract
We consider the downlink of cellular systems in which the users have Quality of Service (QoS) requirements, and we study the design of robust fair broadcasting schemes that maximize the minimum QoS over all users when the users' channel state information (CSI) is imperfect at the transmitter. Using a bounded uncertainty model for the transmitter's estimate of users' channels we formulate each user's QoS requirement as a constraint on the mean square error (MSE) in its received signal, and we demonstrate that these MSE constraints imply constraints on the received signal- to-interference-plus-noise-ratio (SINR) of each user. Using these MSE constraints, we present a unified design approach for robust linear and non-linear transceivers with QoS requirements, and we provide quasi-convex formulations that can be efficiently solved using a one-dimensional bisection search. The proposed designs overcome the limitations of existing approaches that only provide conservative solutions and only applicable to the case of linear preceding. Furthermore, we provide tractable and computationally-efficient design formulations for a quite general model of channel uncertainty that subsumes many uncertainty regions. Our numerical results demonstrate that in the presence of uncertainty in the transmitter's knowledge of users' channels, the proposed designs provide guarantees to a larger set of minimum QoS requirements than existing approaches.
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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".