On MMSE Vector-Perturbation Precoding for MIMO Broadcast Channels With Per-Antenna-Group Power Constraints
Bibliographic record
Abstract
Recently, studies on suboptimal precoding techniques for multiple-input multiple-output broadcast channels (MIMO-BC), which achieve performance near to that of the dirty paper coding (DPC), have drawn attention to vector perturbation (VP) precoding. In practice, each antenna or more generally each antenna group has its own limit on the transmitted power, which makes per-antenna-group power constraints more meaningful than the sum power constraint. In this paper, we introduce an optimization technique for VP precoding employing the minimum mean-square error (MMSE) criterion with per-antenna-group power constraints. This technique is inspired by the p-sphere encoding in a sense that it involves finding the node with the lowest mean-square error (MSE) over a lattice. We demonstrate that the MSE metric, as well as the p-norm one, can be enclosed in a proper Frobenius-norm ball. This Frobenius-norm ball shrinks until it captures the perturbing vector minimizing the MSE. Numerical results show that the proposed algorithm outperforms conventional VP precoding and the p-sphere encoding, but at higher complexity. Consequently, we investigate a couple of simplified techniques employing the MMSE criterion, which perform almost as well as the proposed precoding technique, but are less complex.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 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".