Maximal-ratio eigen-combining: a performance analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| 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 it