On the SNR penalty of MPSK with hybrid selection/maximal ratio combining over i.i.d. rayleigh fading channels
Bibliographic record
Abstract
Closed-form expressions that lower and upper bound the penalty of hybrid selection/maximal ratio combining relative to maximal ratio combining (MRC) for M-ary phase-shift keying (MPSK) modulation are proved. The bounds offer simple-to-evaluate explicit expressions, and are typically within 0.6 dB for hybrid systems with diversity order up to eight that use at least two branches, yet are independent of signal-to-noise ratio (SNR). Contrary to conclusions conjectured in a previously published paper, it is proved that the SNR penalty is not a constant, independent of SNR. It is also shown that previous estimates of the performance losses of selection diversity relative to MRC underestimate or lower bound the losses for MPSK modulation systems, and that the true loss can be significantly larger than previously believed. An upper bound to this loss is also obtained.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".