Performance Analysis of SNR-Based Selection Combining and BER-Based Selection Combining of Signals with Different Modulation Levels in Cooperative Communications
Bibliographic record
Abstract
Cooperative relaying introduces spatial diversity through the creation of a virtual antenna array. The vast majority of the research in digital cooperative relaying assumes the modulation level used by both the source and relay to be the same. This assumption does not necessarily hold when adaptive modulation is implemented. In conventional selection combining, the branch with the highest SNR is chosen; we refer to this scheme as SNR-based selection combining (SNR-SC). In this paper, we introduce BER-based selection combining (BER-SC), as an alternative to SNR-SC, to be used in cooperative communications when a relay may use a modulation level different than that of the source. We provide BER performance analysis for the SNR-SC and BER-SC schemes and show that BER-SC significantly outperforms SNRSC, without any increase in complexity. Moreover, we analytically quantify the gain achieved by using BER-SC over SNR-SC through asymptotic approximation. We note that BER-SC and SNR-SC schemes are identical when the received signals belong to the same modulation level.
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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 | 0.001 | 0.003 |
| 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 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".