Progressive hybrid precoder design for packet retransmissions in large-scale MIMO systems
Bibliographic record
Abstract
We consider progressive hybrid precoder design for packet retransmissions in large-scale multiple-input multiple-output (MIMO) systems with perfect channel state information (CSI) knowledge at the transmitter. To exploit time diversity provided by packet retransmissions, we propose a two-step approach to optimize both the radio-frequency (RF) precoder and the corresponding baseband precoder with the objective of maximizing the mutual information. During each retransmission attempt, we choose the RF precoder matrix columns from the set of transmit array response vectors. For a given RF precoder, we show that the optimal baseband precoder is a function of the generalized eigen-matrix of the Gram matrices of the effective channel matrix and the RF precoding matrix. The optimal baseband precoder for each transmission round includes appropriate power loading, selection and reverse pairing of the singular values of the previous transmission attempts with the ratio of the elements obtained by the diagonalization of the Gram matrices of the effective channel matrix and the RF precoding matrix using a generalized eigen-matrix. Illustrative results show that the proposed progressive hybrid precoder design achieves a performance close to that of the optimal progressive digital precoder (OPDP).
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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".