Effect of Bursty Impulsive Noise on the Performance of Multi-Relay DF Cooperative Relaying Scheme
Bibliographic record
Abstract
In this paper, we consider the performance analysis of a decode-and-forward (DF) cooperative relaying (CR) scheme over bursty impulsive noise channel. As compared to existing literature, here, we generalize the performance analysis to the multi-relay scenario with and without considering error propagation from the relays. For this scheme, we evaluate the bit error rate (BER) performance in the presence of Rayleigh fading with a maximum a posteriori (MAP) receiver. From the obtained results it is seen that, similar to single relay scheme, the proposed MAP receiver attains the lower bound derived for multi-relay DF CR scheme also, and performs significantly better than the conventional schemes developed for additive white Gaussian noise (AWGN) channel and memoryless impulsive noise channel. Moreover, the performance improvement of optimal MAP receiver over the memoryless receivers is substantial with increasing the number of relays.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".