Partial zero-forcing precoding for the interference channel with partially cooperating transmitters
Bibliographic record
Abstract
A communication model is considered in which the classic two-user Gaussian interference channel is augmented by noiseless rate-limited digital conferencing links between the transmitters. We propose a partial zero-forcing precoding strategy based on a shared-private rate splitting scheme at the transmitter, in which each transmitter communicates part of its message to the other transmitter, and subsequently partially pre-subtracts the interfering signal using a zero-forcing precoder. We prove an outer bound and show that the proposed strategy is asymptotically sum-capacity achieving in a very weak interference regime, where both the signal-to-noise ratio (SNR) and the interference-to-noise ratio (INR) go to infinity while their ratio in dB scale is kept fixed. In this case, every cooperation bit results in one-bit gain in sum capacity. We also consider a different asymptotic regime where the transmit power constraints and the channel gains are fixed while the noise powers go down to zero. In this case, if one compares with the achievable sum rate with interference treated as noise, one cooperation bit can in fact result in more than one-bit gain in achievable sum rate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".