Decentralized User Scheduling and Beamforming in Multi-cell MIMO Networks
Bibliographic record
Abstract
We study the problem of distributed user scheduling and beamforming in multi-user, multi-cell, multiple-input multiple-output (MIMO) networks to maximize the weighted sum-rate. While previous work has focused on optimizing the signal-to-leakage-plus noise ratio (SLNR) or the signal-to-interference-plus-noise ratio (SINR), we propose a new signal-to-leakage-plus-interference-plus-noise ratio (SLINR) metric which hybridizes the SINR and SLNR by incorporating the intra-cell interference and inter-cell leakage. Using fractional programming and the Hungarian algorithm, we construct an iterative resource allocator that performs user scheduling and beamforming while accounting for channel estimation errors. Furthermore, we show different approaches for calculating the leakage which vary in terms of practicality and scalability. These approaches decrease the complexity compared to the standard method of leakage calculation, while providing comparable performance. Our results show that resource allocation based on the SLINR metric is a promising solution for decentralized implementation.
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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 | 0.000 |
| 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) | 0.001 | 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".