Bibliographic record
Abstract
Relay network designs have been widely studied in recent years. It is known that cooperative relay network can achieve cooperative diversity with the help of relays and improve the data rate and/or the reliability of the network. On the other hand, green communication design has also attracted significant attention due to the drastic increase in energy consumption. We are going to investigate green communication designs in relay network in our work. In this thesis, we adopt a novel efficiency measure, the power-normalized received signal to noise ratio (PN-SNR) in relay network design for several scenarios and analyze the performance of the proposed designs. In single-relay network and multi-relay network with a sum relay power constraint, the PNSNR maximization problem is formulated and solved. In multi-relay network with individual power constraint on each relay, we investigate both the basic PN-SNR maximization problem and the quality of service (QoS)-constrained PN-SNR maximization problem. Performance of the proposed designs is compared with the fixed relay power scheme and the SNR-maximization scheme analytically and numerically via simulation. Our results show that with the same average relay transmit power, the PN-SNR maximizing scheme is superior to the fixed relay power scheme not only in the PN-SNR but also in the outage probability for both single and multi-relay networks. Compared with SNR-maximizing scheme, it is significantly superior in PN-SNR with moderate degradation in outage probability. Our results reveal the potential of PN-SNR as efficiency measure in relay network design.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".