Fronthaul Compression and Precoding Design for Full-Duplex Cloud Radio Access Network
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, joint design of fronthaul compression and precoding is studied for full-duplex (FD) cloud radio access networks. Multiple uplink and downlink users equipped with multiple antennas communicate with a control unit (CU) in the “cloud” through a set of multiantenna FD radio units that are connected to the CU through limited capacity fronthaul links. In the first part of this paper, we address the weighted sum-rate maximization problem, to compute the optimal precoding and the quantization noise covariance matrices. By exploiting the relationship between weighted-sum-rate maximization and weighted minimum-mean-square-error minimization problems, and leveraging the successive convex approximation (SCA) method, we propose an iterative algorithm that guarantees convergence to a stationary point. In the second part of this paper, we address the stochastic sum-rate maximization problem under fast-fading channels, where only the statistics of the channel state information is available. Casting this nonconvex problem as a difference of convex problem, an iterative algorithm based on the combination of stochastic successive upper bound minimization and SCA approaches that guarantees convergence to a stationary point is proposed. Numerical results demonstrate the advantage of the proposed algorithms.
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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 | 0.001 | 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 | 0.001 | 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