Node Switching Rates of Opportunistic Relaying and Switch-and-Examine Relaying in Rician and Nakagami-m Fading
Bibliographic record
Abstract
In opportunistic relaying systems, only the relay with best channel condition among K relays is selected to take part in cooperation. This setup efficiently achieves diversity gain. However, the high switching rate of such systems may be undesirable due to practical implementation issues, for example, the corruption of the data signal by receiver switching transients, channel estimation and synchronization failures due to excessive switching as well as network control switching overheads which increase with increased switching. Recently, switch-and-examine relaying whose main advantage is its low switching rate, was proposed as a low complexity suboptimal alternative to opportunistic relaying. Meanwhile, comparisons of the switching rates of opportunistic and switch-and-examine schemes have been undertaken only for the case of Rayleigh fading. The switching rates of opportunistic relaying and switch-and-examine relaying systems with two or more relays operating under Rician and Nakagami-m fading are obtained in closed-form or single integral expressions. Results for independent and identically distributed fading links are obtained for the case of multiple relays and additionally for independent but not identically distributed fading links for the two relays case. The closed-form solutions explicitly depend on the Doppler frequency of the fading.
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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 | 0.015 |
| 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 | 0.001 | 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".