The Capacity Region of p-Transmitter/q-Receiver Multiple-Access Channels\n with Common Information
Bibliographic record
Abstract
This paper investigates the capacity problem for some multiple-access\nscenarios with cooperative transmitters. First, a general Multiple-Access\nChannel (MAC) with common information, i.e., a scenario where p transmitters\nsend private messages and also a common message to q receivers and each\nreceiver decodes all of the messages, is considered. The capacity region of the\ndiscrete memoryless channel is characterized. Then, the general Gaussian fading\nMAC with common information wherein partial Channel State Information (CSI) is\navailable at the transmitters (CSIT) and perfect CSI is available at the\nreceivers (CSIR) is investigated. A coding theorem is proved for this model\nthat yields an exact characterization of the throughput capacity region.\nFinally, a two-transmitter/one-receiver Gaussian fading MAC with conferencing\nencoders with partial CSIT and perfect CSIR is studied and its capacity region\nis determined. For the Gaussian fading models with CSIR only (transmitters have\nno access to CSIT), some numerical examples and simulation results are provided\nfor Rayleigh fading.\n
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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 | 0.011 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".