Performance of decode-and-forward cooperative relaying over Rayleigh fading channels with impulsive noise
Bibliographic record
Abstract
This paper presents the performance analysis of a decode-and-forward (DF) cooperative relaying (CR) scheme using quadrature amplitude modulation (QAM) in the presence of Rayleigh fading and Bernoulli-Gaussian impulsive noise. The exact symbol error probability (SEP) expression for direct transmission (DT) and SEP lower bound for DF-CR are first derived and then used to establish the optimum power allocation (OPA) for the source and the relay by exhaustive search. Analytical and simulation results for various scenarios with DT and DF-CR under the same bandwidth efficiency and power consumption are in good agreement and indicate that the lower bound SEP is very tight for the optimal Bayes receiver and CR in an impulsive noise environment can be beneficial at a certain degree depending on impulse power and impulse rate. Furthermore, OPA brings a negligible performance improvement as compared to equal power allocation under investigated conditions.
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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 | 0.000 |
| 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".