Performance Analysis of Decode-and-Forward Incremental Relaying Cooperative-Diversity Networks over Rayleigh Fading Channels
Bibliographic record
Abstract
Cooperative-diversity networks have recently been proposed as a way to form virtual antenna arrays without using collocated multiple antennas. Cooperative-diversity networks use the neighbor nodes to assist the source by sending the source information to the destination for achieving spatial diversity. Regular cooperative-diversity networks make an inefficient use of the channel resources because relays forward the source signal to the destination every time regardless of the channel conditions. Incremental relaying cooperative diversity has been proposed to save the channel resources by restricting the relaying process to the bad channel conditions only. Incremental relaying cooperative relaying networks exploit limited feedback from the destination terminal, e.g., a single bit indicating the success or failure of the direct transmission. If the destination provides a negative acknowledgment via feedback; in this case only, the relay retransmits in an attempt to exploit spatial diversity by combining the signals that the destination receives from the source and the relay. In this paper, we study the end-to-end performance of incremental relaying cooperative-diversity networks using decode-and-forward relays over independent non-identical Rayleigh fading channels. Closed-form expressions for the bit error rate and the signal-to-noise ratio (SNR) outage probability are determined. Results show that the incremental relaying cooperative diversity can achieve the maximum possible diversity, compared with the regular cooperative-diversity networks, with higher throughput.
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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 | 0.008 |
| 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 | 0.000 |
| 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".