Vector perturbation precoding and user scheduling for network MIMO
Bibliographic record
Abstract
In this paper, we apply vector perturbation (VP) precoding to a network multiple-input multiple-output (MIMO) scheme, in which downlink transmissions from base stations are coordinated. We propose a multi-cell VP by introducing a common power scaling factor for all base stations in order to satisfy per base station power constraint. In our scenario, we consider multiple-antenna users with heterogeneous signal-to-noise ratios (SNRs). Our work is an extension of earlier work on VP to a multi-cell network with multiple-antenna users. The sum rate for the multi-cell VP in the case of uniformly distributed input is obtained and an asymptotic upper bound for it is proposed. The results show that the multi-cell VP is superior to the multi-cell block diagonalization (BD). By using the upper bound on the sum rate, we propose a user scheduling algorithm, which provides better performance and is less complex than semi-orthogonal user selection in the case of multiple-antenna users (SUS-MA).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".