Joint User Grouping, Sparse Beamforming, and Subcarrier Allocation for D2D Underlaid Cache-Enabled C-RANs With Rate Splitting
Bibliographic record
Abstract
We propose a Rate Splitting (RS) transmit scheme for Device-to-Device (D2D) underlaid Cache-enabled Cloud Radio Access Networks (C$^2$-RANs). To this end, we jointly design Cellular User (CU) grouping, dynamic Remote Radio Head (RRH) clustering, beamforming, RS ratio, and subcarrier allocation to maximize the sum-rate and ensure transmit power and fronthaul cost constraints. However, the formulated problem is discrete, non-smooth, and non-convex. We thus decouple it into three subproblems, namely - (1) CU grouping, (2) D2D subcarrier allocation, and (3) joint sparse beamforming, RS ratio, and power control. We then develop a low-complexity greedy searching algorithm and a fairness-ensuring accelerated bisection searching algorithm via graph theory for the first subproblem. For the second subproblem, we use a many-to-one matching game with peer effect. We adopt the Gale-Sharply algorithm and swap operations to reach a stable matching state and propose a Two-sided Stable Subcarrier Allocation ($\text{TS}^2$A) algorithm. For the third subproblem, we develop a Quadratic Transform-based Two-Tier Alternating ($\text{QT}^3$A) algorithm. It constructs a series of accurate surrogate functions for the objective function and constraints via a Quadratic Transform (QT) technique. We then recast it as a two-tier alternating problem. We tackle the outer and inner tiers with closed-form expressions and the convex framework, respectively. By integrating these algorithms, the overall algorithm can be proved to converge to a stationary point. Simulation results show that it achieves considerable performance gains over several benchmark schemes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".