Bibliographic record
Abstract
The main objective of this study is to investigate the structures and procedures which are promising to achieve higher rates and larger rate regions in the relay channels and the communication networks that contain relay nodes.In particular, the study places particular emphasis on the relaying techniques pertaining to compressand-forward.The thesis first examines a generalization of decode-and-forward (DF) and compress-and-forward (CF) in Gaussian channels.Although this generalization has been known for over thirty years, the result in this thesis is the first to illustrate the signal-to-noise ratio (SNR) regions in which the generalization reduces to constituent DF or CF schemes.In particular, the thesis demonstrates the existence of SNR regions in which the generalization is guaranteed to supersede both DF and CF, but with a gain of within 0.5 bits per channel use.Having gained insight into the random binning in the CF scheme, this thesis argues that a new decoding procedure exploiting the N-to-1 mapping based on binning is able to relax the rate constraint on the relay transmission, and generalize the noisy network coding based schemes which are constrained to the 1-to-1 mapping.This thesis identifies two instances in which exploiting the N-to-1 mapping inherent in this generalization yields rate gains, even though it does not yield such a gain in other multimessage networks.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".