Diversity combining and eigencombining performance and complexity comparison for estimated channels
Bibliographic record
Abstract
Conventionally, antenna arrays apply maximal-ratio combining (MRC) directly to received signals. However, the performance of MRC compensates for its high numerical complexity only for low antenna correlation, i.e., rarely, for space-limited base-stations in actual scenarios, where the azimuth spread (AS) is random and predominantly small. Maximal-ratio eigencombining (MREC), i.e., MRC of the outputs of a partial Karhunen-Loeve Transform (KLT) of the received signals, can attain available diversity gain more efficiently. Herein, MREC is adapted to the AS with a bias-variance trade-off criterion (BVTC) that selects for KLT dominant eigenvectors of the channel correlation matrix that are computed with a deflation-based projection-approximation subspace tracking (PASTd) algorithm. Optimum and suboptimum channel fading estimation and weight-signal combining are evaluated. Simulations indicate that PASTd-BVTC-based suboptimum MREC with optimum fading estimation can perform similarly to the much more complex optimum-MRC approach.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".