On the Capacity Gap of Gaussian Multi-Way Relay Channels
Bibliographic record
Abstract
Multi-way relaying is a promising approach to enhance the spectral efficiency in multi-user communication systems. Several relaying strategies have been proposed recently to be used in multi-user communication systems. In this paper, we analyze the gap between the achievable rate of some of these relaying techniques and the capacity of the Gaussian multiway relay channels (GMWRCs). To this end, for a symmetric GMWRC with K users, we prove that lattice-based relaying guarantees a gap less than 1/2(K-1) bit from the capacity upper bound. Also, we show that decode-and-forward and amplify-and-forward relaying may have a larger capacity gap than 1/2(K-1) bit depending on the relay and users' SNR. Then, we find the SNR regions where these two techniques also ensure a 1/2(K-1)-bit gap.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".