MMSE-based MIMO precoder using partial channel information
Bibliographic record
Abstract
This paper presents a precoder design based on the minimum mean squared error (MMSE) criterion and using the knowledge of only the transmit and receive correlation matrices of the underlying MIMO channel. The optimal transformations are shown to be eigen-beamformers, which transmit the signal along eigenvectors of the transmit correlation matrix. Power loading across the eigenbeams are determined based on eigenvalues of both transmit and receive correlation matrices and can be viewed as a waterpouring policy. Performance of the proposed precoders using partial channel information in various MIMO channels is evaluated and compared to that of precoders based on full channel knowledge at transmitter. It was shown that as the number of the transmit and receive antennas increases, the MMSE-based MIMO precoders using partial channel knowledge can achieve the performance of precoders using full channel knowledge. Furthermore, the channel that gives the best performance is the one with only one strong eigen mode for cases with a large number of receive antenna or a small number of transmit antenna. For small number of receive antennas or large number of transmit antennas, the full rank channel with equal gain eigen modes will gives the best performance.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".