Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, by considering multiple slices, a downlink transmission of a sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is investigated. In this setup, by assuming multiple-input and single-output (MISO) transmission mode, a novel robust radio resource allocation is proposed where considering uncertain channel state information, the worst case approach is applied. We consider a radio resource allocation problem with the objective to maximize the total sum rate of users subject to a minimum required rate of each slice and practical limitations of C-RAN and SCMA. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where beamforming and joint codebook allocation and user association subproblems are sequentially solved. By introducing auxiliary variables, the joint codebook allocation and user association subproblem is transformed into an integer linear programming, and to solve the beamforming optimization problem, minorization-maximization algorithm is applied. Via numerical results, the performance of the proposed algorithm is investigated versus different uncertainty level for different system parameters.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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