Bibliographic record
Abstract
In a denoise-and-forward (DNNF) two-way relay system, since the relay is at different distances with the end users, the signals are combined with asynchronous phases and delays, leading to severe performance loss. For most denoising and decoding methods, precise estimation of the delay is required by the relay and end users, which is usually unavailable when strong noise is present. Using the ergodic property of chaotic signals and the fact that the sum of two signals over a given period is invariant with respect to their temporal alignment, we propose to address the asynchronous problem using chaos modulation. In particular, we use ergodic chaotic parameter modulation (ECPM) and guarding intervals (GIs) to remove the requirement for precise time synchronization and complex iterative decoding. The theoretical bit-error-rate (BER) performance is analyzed and verified by simulations. A relay selection method is also proposed for two-way relay systems with multiple relays to use part of the relays to achieve improved performance compared to using all relays. It is shown that the relay selection method can increase the normalized throughput in low signal-to-noise ratio (SNR) scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".