Threshold-based Adaptive Decode-Amplify-Forward relaying protocol for cooperative systems
Bibliographic record
Abstract
In this paper, we propose a new adaptive relaying protocol called Threshold-based Adaptive Decode-Amplify- Forward relaying protocol (T-ADAF). In our protocol, the relay compares the signal to noise ratio (SNR) of the received signal to the average SNR of the source-relay link. If the SNR of the received signal is greater than the average SNR of the source-relay link, then the relay performs the Amplify-Forward relaying protocol (AF). On the other hand, if the SNR of the received signal does not exceed the average SNR of the source-relay link, then the relay performs the Adaptive Decode-Forward relaying protocol (ADF). The performance of the proposed protocol is investigated and a closed form of its symbol error probability is derived in the presence of Rayleigh fading channels. Furthermore, a comparison with other relaying protocols such as AF, ADF and SNR-HDAF (SNR-based Hybrid Decode-Amplify-Forward) is made in order to evaluate the performance of our protocol and to show its benefits. We also investigate the T-ADAF protocol with multiple relays and we derived a closed form of its symbol error probability.
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 | 0.001 | 0.003 |
| 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 | 0.002 | 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".