SMSE precoder design in a multiuser MISO system with limited feedback
Bibliographic record
Abstract
We design an end-to-end linear transceiver in the downlink of a multi-user (MU) multiple input single output (MISO) system with quantized channel state information at the transmitter (CSIT). The design minimizes the sum mean squared error (SMSE) under a sum power constraint. The contribution of this paper is two-fold. First, unlike previous approaches, we quantize the channels using mean squared inner product (MSIP) vector quantization (VQ) and derive an SMSE-based algorithm that considers MSIP quantization error as an integral component of the whole system. This decreases the bit error rate (BER) at high signal-to-noise ratio (SNR) and outperforms previously derived MU MISO linear transceivers that exist in the limited feedback literature. Second, we show analytically why the BER, in the high SNR regime, increases if quantization error is not considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".