Joint User Scheduling and RRU Association for ODFMA-Based Networks With Inter-BBU Coordination
Bibliographic record
Abstract
This paper investigates the joint user scheduling (US) and user association (UA) problem for OFDMA-based networks under the centralized radio access network (C-RAN) architecture. We first formulate the joint optimization problem for US and UA, which is intractable in its exact form, and therefore, we propose a corresponding upper bound problem, with much lower computational complexity, with the help of a novel continuous rate function. We show that the upper bound problem can be further converted into an equivalent convex optimization problem via geometric programming (GP) that can be solved with inter-baseband unit coordination, which is viable in the C-RAN architecture. Furthermore, we show that the solutions of the convex upper bound problem can be mapped into the solution space of the original joint US and UA problem with a small gap. As a practically implementable solution to the original problem, a heuristic-based scheduler has been developed to obtain quasi-optimal UA and US solutions for the uplink (UL) as well as the downlink (DL) transmissions. Through extensive numerical simulations, we verify that the performance of the proposed heuristic-based scheduler is quasi-optimal on both UL and DL.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".