Bibliographic record
Abstract
This chapter unifies the principles and analyses of conventional signal processing algorithms for receive-side smart antennas, and compares their performance and numerical complexity. The chapter starts with a brief look at the traditional single-antenna optimum symbol-detector, continues with analyses of conventional smart antenna algorithms, i.e., statistical beamforming (BF) and maximal-ratio combining (MRC), and culminates with an assessment of their recently proposed superset known as eigencombining or eigenbeamforming. BF or MRC performance fluctuates with changing propagation conditions, although their numerical complexity remains constant. Maximal-ratio eigencombining (MREC) has been devised to achieve best (i.e., near-MRC) performance for complexity that matches the actual channel conditions. The authors derive MREC outage probability and average error probability expressions applicable for any correlation. Particular cases apply to BF and MRC. These tools and numerical complexity assessments help demonstrate the advantages of MREC versus BF or MRC in realistic scenarios.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".