Coordinated uplink scheduling and beamforming for wireless cellular networks via sum-of-ratio programming and matching
Bibliographic record
Abstract
This paper proposes a joint uplink user scheduling and beam-forming algorithm for a multiple-antenna wireless cellular network. We show that coordinated optimization across the cells can significantly alleviate intercell interference, thereby improving the cell-edge rates in a multicell network. Unlike the downlink case, coordinating uplink transmission in a multicell network is significantly more challenging, because uplink interference depends strongly on the schedule and beamformers of neighboring cells. The main contribution of this paper is the recasting of the problem in terms of sum-of-ratio programming and a subsequent quadratic reformulation which allows scheduling and beamforming to be optimized through solving a matching problem. This problem reformulation also provides a new interpretation of the well-known weighted minimum mean square error (WMMSE) algorithm. Simulation results show that the proposed approach significantly outperforms both the WMMSE algorithm and the existing uncoordinated scheduling approach.
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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 | 0.001 |
| 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".