On the Capacity of Gaussian Multiple-Access Interference Channels
Bibliographic record
Abstract
We study the multiple-access interference channel (MAIC) where two two-user multiple-access channels (MAC) operate over a shared medium, thus mutually interfering. We first characterize an achievable region for the discrete memoryless MAIC based on the Han-Kobayashi scheme. For the Gaussian MAIC, a computable achievable region is provided by limiting the time-sharing variable to be binary, and depending on its binary state, the transmitters use a fixed private-common message power splitting. Focusing on the weak Gaussian MAIC, where the magnitude of the coefficient (gain) of the cross-channels are smaller than that of the direct-channels, two genie-aided outer bounds to the sum-rate are derived by providing noisy interfering signals as side-information. Numerical examples in the weak symmetric Gaussian MAIC shows that (i) the outer bounds improve upon previous bounds in many cases, and (ii) for a wide range of cross-channel gains, the achievable sum-rate and one of the outer bounds differ by a small numerical gap, thus providing good approximations to the sum-capacity in these cases.
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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 | 0.000 | 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".