Cooperative diversity in the presence of impulsive noise
Bibliographic record
Abstract
Although there already exists a rich literature on cooperative diversity, current results are mainly restricted to the conventional assumption of additive white Gaussian noise (AWGN). AWGN model realistically represents the thermal noise at the receiver, but ignores the impulsive nature of atmospheric noise, electromagnetic interference, or man-made noise which might be dominant in many practical applications. In this paper, we investigate the performance of cooperative communication over Rayleigh fading channels in the presence of impulsive noise modeled by Middleton Class A noise. Specifically, we consider a multi-relay network with amplify-and-forward relaying. Through the derivations of pairwise error probability, we quantify the diversity advantages. Based on the minimization of a union bound on the error rate performance, we formulate optimal power allocation schemes and demonstrate significant performance gains over their counterparts with equal power allocation. An extensive Monte Carlo simulation is also presented to illustrate the performance of cooperative schemes in various impulsive environments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".