Diversity order analysis of the decode-and-forward cooperative networks with relay selection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we focus on the diversity order of the decode-and-forward (DF) cooperative networks with relay selection. Many detection schemes have been proposed for the DF; but it has been shown that the cooperative maximum ratio combining (C-MRC) can achieve almost the same performance as the optimum maximum likelihood detector and has a much lower complexity. Therefore, we first combine the C-MRC with the relay selection and show that it achieves the full diversity order by deriving an upper bound of its average bit error rate (BER). In order to reduce the signaling overhead, we then combine the link-adaptive regeneration (LAR) with the relay selection. By deriving an upper bound of the average BER, we show that, when there are two relays, the diversity order of the LAR with relay selection is upper-bounded by three and lower-bounded by 3 - epsiv, where xi is an arbitrarily small positive number.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 it