Performance and Optimization of Amplify-and-Forward Cooperative Diversity Systems in Generic Noise and Interference
Bibliographic record
Abstract
Cooperative diversity 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 cooperative diversity system consisting of a source, a destination, and multiple single-hop amplify-and-forward relays, and provide a mathematical framework for the asymptotic analysis of this system in generic noise and interference for high signal-to-noise ratios. Assuming independent Rayleigh fading for all links in the network and orthogonal relay-destination channels, we obtain simple and elegant closed-form expressions for the asymptotic symbol and bit error rates valid for arbitrary linear modulation formats, arbitrary numbers of relays, and arbitrary types of noise and interference with finite moments including co-channel interference, ultra-wideband interference, impulsive ε-mixture noise, generalized Gaussian noise, and Gaussian noise. Furthermore, we exploit the derived analytical error rate expressions to develop power allocation, relay selection, and relay placement schemes that are asymptotically optimal in environments with generic noise and interference. In general, the power allocation problem results in a geometric program which can be solved efficiently numerically. For the special case of only one relay, we provide a closed-form result for the optimal power allocation. Simulation results confirm our analysis and illustrate that, in non-Gaussian noise, the proposed power allocation, relay selection, and relay placement schemes lead to large performance gains compared to their conventional counterparts 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".