A Criterion for Determining the Optimal Position of the Demodulate-and-Forward (DMF) Relay
Bibliographic record
Abstract
This paper considers the issue of selecting the optimal position where a Relay could be fixed to enable the communication between a Source and a Destination. Our analysis is focused on the case when Relay works on Demodulate-and-Forward (DMF) mode, due its simple nature in implementation. DMF Relay only demodulates the received signals from the Source without checking the integrity of the signals and it may happen that it forwards the erroneous signals toward the Destination. Since moving the Relay in different positions between the Source and the Destination brings different performances, we are interested to find the position at which the benefit of using the Relay is maximized. To help us find this optimal position analytically and numerically, we have introduced a criterion which relies on the trade-off between the demodulation errors on the channel S-R and the ability of the Relay to succeed on the transmission path R-D, where the theoretical analysis of the criterion matches perfectly with the simulation analysis. Moreover, in a scenario of having more than one Relay, this criterion helps us to choose the best instantaneous Relay for cooperation. The comparison of our proposed criterion with a referent criterion from the literature, has shown that our criterion outperforms the referent criterion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".