Performance of Cooperative Diversity Systems in Non-Gaussian Environments
Bibliographic record
Abstract
Cooperative diversity (CD) systems have received significant attention recently as a distributed means of exploiting the inherent spatial diversity of wireless networks. In this paper, we consider a CD system where a source transmits to a destination via multiple single--hop amplify and forward (AF) relays. In particular, we provide a mathematical framework for the asymptotic analysis of this system in general non--Gaussian noise and interference for high signal--to--noise ratios (SNRs). Assuming independent Rayleigh fading for all links in the network, we obtain simple and elegant closed--form expressions for the asymptotic symbol error rate (SER) and bit error rate (BER) valid for arbitrary linear modulation formats, arbitrary numbers of relays, and arbitrary non--Gaussian noise and interference with finite moments. Furthermore, exploiting the derived analytical results, we introduce a new relay selection criterion for non--Gaussian environments. Simulation results confirm our analysis and illustrate that, in non--Gaussian noise, the proposed relay selection criterion can lead to large performance gains compared to the conventional relay selection criterion which was optimized for Gaussian noise.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".