Maximal-ratio eigen-combining: a performance analysis
Bibliographic record
Abstract
Maximal-ratio eigen-combining (MREC) for wireless communications channels, also known as eigen-beamforming for receivers equipped with antenna arrays, integrates conventional maximum average signal-to-noise-ratio beamforming (Max-ASNR BF) and maximal-ratio combining (MRC) to provide both high average SNR in high fading correlation as well as diversity in low fading correlation. Previous studies of MREC were based on simulation or limited analysis and suggested that MREC can outperform Max-ASNR BF and MRC in terms of average error probability (AEP). A comprehensive analysis of MREC is provided for BPSK signals and Rayleigh fading, including computable AEP and outage probability (OP) expressions for perfectly known, correlated channel gains. Particular cases of these expressions apply to Max-ASNR BF and MRC. For imperfectly known channels the analysis yields a new and general AEP expression for MREC, which is specialized to estimation based on pilot-symbol-aidedmodulation (PSAM) and interpolation. In particular, this AEP expression applies to Max-ASNR BF and, for PSAM and data-independent interpolation filters, to MRC. Numerical results for antenna arrays receiving signals with angle-of-arrival dispersion and imperfectly known channel gains confirm the potential advantage of MREC over Max-ASNR BF and MRC.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 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) | 0.004 | 0.002 |
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".