Vector Perturbation Precoding Under Quantized CSI
Bibliographic record
Abstract
This paper focuses on the design of vector perturbation (VP) precoding for multiuser multiple-input-single-output (MU-MISO) downlink transmission under quantized channel side information (CSI). In particular, each receiver decomposes its downlink channel vector in forms of channel direction information (CDI) and channel magnitude information (CMI) for feedback to the transmitter. The CMI contribution is studied in two scenarios: i) perfect CMI available to the transmitter and ii) only CMI statistics known at the transmitter. Under these two scenarios, using the quantized CDI and quantization error statistics, we propose a unified approach to design the VP precoders that minimize the mean square error (MSE) between channel input and output. Closed-form expressions to the precoders are then derived. Bit-error-rate (BER) results indicate that the proposed VP precoder designs are less sensitive to quantization errors, and CMI availability offers significant performance improvements.
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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 | 0.001 |
| 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".