Performance of Non-Symmetric Relaying Networks in the Presence of Interferers with Unequal Powers
Bibliographic record
Abstract
The performance of non-symmetric multiple-hop multiple-branch relaying networks using amplify-and-forward (AF) protocol and operating in practical environments with unequal-power interferers, is examined. Assuming the channels, for both the desired and the interfering signals, to experience Rayleigh fading, first, exact and upper-bound expressions for the end-to-end signal-to-interference-plus-noise ratio (SINR) are derived. Then, the moment generating function of the upper-bound end-to-end SINR is obtained. According to the latter, the error and outage probabilities are assessed in closed form. Further, simple and general asymptotic expressions for the error and outage probabilities, which explicitly show the coding and the diversity gains, are derived and discussed. Finally, the analysis is validated by comparing the corresponding numerical results with Monte Carlo simulations, sustained by insightful discussions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| 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".