On Resource Allocation for Downlink Power Minimization in OFDMA Small Cells in a Cloud-RAN
Bibliographic record
Abstract
We consider the problem of minimizing the total downlink transmit power in an orthogonal frequency-division multiple access (OFDMA)-based cellular network composed of a single-antenna macrocell overlaid by multi-antenna small cells deployed in a cloud radio access network (C-RAN) architecture. More specifically, the C- RAN minimizes the total downlink transmit power subject to the quality of service (QoS) constraints for small cell user equipments (SUEs), power budgets of small cells, interference thresholds for macro UEs (MUEs), and practical fronthaul capacity constraints. This problem is a mixed integer nonlinear problem (MINLP). Moreover, the problem can become infeasible which necessitates the employment of some form of admission control (AC). Therefore, relying on the framework of successive convex approximation (SCA), we propose a low-complexity solution for the original non- convex MINLP by solving a series of convex problems, which is guaranteed to converge to a local optimum solution. Numerical results indicate the performance of the proposed formulation and demonstrate the underlying tradeoffs among the SUEs' QoS requirements, number of admitted SUEs, total downlink transmit power, and the available fronthaul capacity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".