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 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".