SER and Outage of Threshold-Based Hybrid Selection/Maximal-Ratio Combining Over Generalized Fading Channels
Bibliographic record
Abstract
The average symbol-error rate and outage probability of threshold-based hybrid selection/maximal-ratio combining (T-HS/MRC) in generalized fading environments are analyzed. A T-HS/MRC combiner chooses the combined branches according to a predetermined normalized threshold and the strength of the instantaneous signal-to-noise ratio (SNR) of each branch. Therefore, the number of combined branches is a random variable, rather than a fixed number, as in conventional hybrid selection/maximal-ratio combining (H-S/MRC). Using the moment generating function method, a unified analysis of T-HS/MRC over various slow and frequency-nonselective fading channels is presented. Both independent, identically distributed and independent, nonidentically distributed diversity branches are considered. The derivation allows different M-ary linear modulation schemes. The theory is illustrated using coherent M-ary phase-shift keying in Nakagami-m fading as an example. It is shown that previous published results are incorrect.
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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 | 0.004 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".