Selection Combining of Signals with Different Modulation Levels in Nakagami-m Fading
Bibliographic record
Abstract
Conventionally, the BER analysis of selection combining schemes is performed under the assumption that the signals to be combined belong to the same modulation level. This is not necessarily the case in cooperative relaying, where the source and relay may use different modulation levels. Recently, a closed-form BER expression was derived for selection combing of signals with different modulation levels, under the assumption that the source-relay, source-destination, and relay-destination links are all modeled as Rayleigh fading channels. In this letter, we extend the analysis to the asymmetric Nakagami-m fading channel, which is a more versatile channel model. We derive the closed-form BER expression which is expressed in terms of elementary functions. The derived BER expression generalizes many existing expressions in the literature. Simulation results are also presented to confirm the accuracy of the derived results.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".