Performance Analysis of 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 research in bit-error-rate (BER) performance analysis of selection-combining (SC) schemes used in digital cooperative relaying assumes the modulation level used by both the source and the relay to be the same. This assumption does not necessarily hold when adaptive modulation is implemented. In conventional SC, the branch with the highest signal-to-noise ratio (SNR) is chosen; we refer to this scheme as SNR-based SC (SNR-SC). However, when different modulation levels are employed, the branch that has the maximum SNR may not necessarily be the most reliable branch due to different error-resistance capabilities of the modulation levels. Consequently, the BER-based SC (BER-SC) is a better SC scheme. In BER-SC, the receiver calculates the BER for each branch (using the SNR and the modulation level) and then decodes the signal from the branch that has the minimum BER. In this paper, we provide BER performance analysis for both BER-SC and SNR-SC and show that BER-SC outperforms SNR-SC, with very comparable 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".