Gaussian multiple-access relay channels with non-causal side information at the transmitters
Bibliographic record
Abstract
The multiple-access relay channel (MARC) and its Gaussian version are important models in cellular, ad hoc communication systems, and sensor networks and also, this channel is a comprehensive model which consist of two important channels: Relay Channel (RC) and Multiple Access Channel (MAC). In this paper, we study and analyse the two-user state-dependent discrete and memoryless MARC in which the independent states of channel are known non-causally only at the encoders. An achievable rate region by using binning and decode-and-forward (DF) schemes and an outer bound for this model are obtained. We also by using our results obtain an inner bound for two-user Gaussian MARC with identical non-causal side information. Our model includes discrete and continuous (Gaussian) multiple access channel rate region and relay channel rate with non-causal side information. Finally, we evaluate our bounds for Gaussian MARC numerically and illustrate the effect of interference power on bounds.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".