Noncoherent relay selection for bidirectional cooperative networks
Bibliographic record
Abstract
In this paper we propose a new noncoherent relay selection strategy for two-way relay cooperative networks. In our proposed scenario, channel state information (CSI) or feedback link is not needed, neither for relay selection nor for data detection. We assume Mary frequency shift keying (FSK) modulation for our transmission scheme and decode-and-forward (DF) protocol for our system relaying method. Relay selection is performed based on a min-max criterion on the received instantaneous signal energies at the relay nodes. Furthermore, by using energy detectors at both destination nodes the transmitted signal is decoded. We present a closed-form formula for the average symbol error rate (ASER) of the proposed noncoherent selection method. We also provide high signal-to-noise-ratio (SNR) analysis and the achievable diversity order for the proposed selection method. Finally we present a simulation study to corroborate the analytical results and to demonstrate the performance of the introduced noncoherent selection method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".