Low Complexity Selection Cooperation Techniques Using Information Accumulation in Dual-Hop Relaying Networks
Bibliographic record
Abstract
Three low complexity, single-parameter, selection cooperation protocols, called P-n, P-γ, and P-t, are introduced for dual-hop relaying networks. The protocols are based on information accumulation, and can potentially be implemented using rateless codes. The expected transmission time is analyzed for the three protocols for block fading channels. As a baseline for performance comparison, a rate optimal protocol, called P-o, is proposed. In the single-relay case, all the protocols, if used with their optimized parameter which is trivially obtained, coincide with P-o. Large signal-to-noise ratio (SNR) approximations to the optimal parameters in the multirelay case, which have good accuracy in large SNR regimes, and satisfactory accuracy for smaller values of SNR, are derived. The dependence of the optimal parameters on the network parameters is numerically studied for Rayleigh fading. The average rate and average source transmission time (ASTT) of the schemes are compared with one another and other comparable previous relaying schemes through numerical examples. It is observed that the suboptimal schemes exhibit near-optimal rate and ASTT performances. As a general rule, P-o and P-γ have the best performances. The P-n scheme has a larger rate, but a larger or smaller ASTT, compared to P-t. Increasing the number of relays ultimately causes diminishing returns in the average rate and ASTT.
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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 | 0.002 | 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 | 0.000 | 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) | 0.000 | 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 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".