SLINR-Based Downlink Optimization in MU-MIMO Networks
Bibliographic record
Abstract
Optimizing the downlink of multi-cell multiuser multiple input multiple output (MU-MIMO) networks has received substantial attention; however, the schemes in the literature consider centralized solutions requiring significant overhead in information exchange (e.g., global channel state information or CSI) and computation load (the need to solve a single large problem). This paper presents adecentralizedweighted sum-rate (WSR) maximization algorithm for the multiuser downlink, accounting for beamforming, scheduling, and power allocation. We show that the signal-to-leakage-plus-noise ratio (SLNR) used in previous work suffers from significant drawbacks that limit its potential use in WSR maximization. We address this by proposing a new performance measure, the signal-to-leakage-plus-interference-plus-noise ratio (SLINR), which incorporatesintra-cell interferenceandinter-cell leakage. The SLINR exploits the benefits of the SLNR approach, but by explicitly including interference, avoids many of its flaws. We derive an iterative and decentralized resource allocation approach under imperfect CSI, and our simulation results show that, despite BSs using onlylocal information, the proposed algorithm comes within 3.8% of the throughput achieved by centralized schemes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".