Decentralized User Scheduling and Beamforming in Multi-cell MIMO Networks
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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