Performance of Orthogonal Wireless Relay Networks with Multiple SNR-Thresholds and Multiple Hard-Decision Detections
Bibliographic record
Abstract
This paper investigates the diversity performance of wireless relay networks with multiple parallel relays communicating with the destination over orthogonal channels. The networks under consideration employ two signal-to-noise ratio (SNR) thresholds and multiple hard-decision detections (HDD) at the destination. One SNR threshold is used to select transmitting relays in the second phase: a relay retransmits to the destination if its received SNR is larger than the threshold, otherwise, it remains silent. The other threshold is used at the destination for detection: the destination makes a hard decision on the received signal from a relay if its SNR is higher than the threshold, otherwise, the destination makes an erasure decision. Then the destination simply combines all the hard-decision results and makes the final binary decision based on majority voting. Focusing on the decode-and-forward (DF) relaying protocol, the paper derives the end-to-end bit error and outage probabilities, and presents the diversity analysis of the proposed method. It is shown that the full diversity order can be achieved by setting appropriate thresholds even when the destination does not know the exact or average SNRs of the source-relay links. The diversity analysis is further extended to multi-hop cooperation and/or with the presence of a direct link where multiple thresholds are needed.
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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 | 0.004 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".