Robust Beamforming Design in C-RAN With Sigmoidal Utility and Capacity-Limited Backhaul
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we study the robust beamforming design in cloud radio access networks, where remote radio heads (RRHs) are connected to a cloud server that performs signal processing and resource allocation in a centralized manner. Different from traditional approaches adopting a concave increasing function to model the utility of a user, we model the utility by a sigmoidal function of the signal-to-interference-plus-noise ratio (SINR) to capture the diminishing utility returns for very small and very large SINRs in real-time applications (e.g., video streaming). Our objective is to maximize the aggregate utility of the users while considering the imperfection of channel state information (CSI), limited backhaul capacity, and minimum quality of service requirements. Because of the sigmoidal utility function and some of the constraints, the formulated problem is non-convex. To efficiently solve the problem, we introduce a maximum interference constraint, transform the CSI uncertainty constraints into linear matrix inequalities, employ convex relaxation to handle the backhaul capacity constraints, and exploit the sum-of-ratios form of the objective function. This leads to an efficient resource allocation algorithm, which outperforms several baseline schemes, and closely approaches a performance upper bound for large CSI uncertainty or large number of RRHs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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