Rate-splitting and successive decoding for Gaussian interference channels
Bibliographic record
Abstract
Joint decoding is employed in many coding schemes to achieve a larger rate region. However, decoding complexity escalates considerably when joint decoding is used. This paper studies the achievable sum-rate of the two-user Gaussian interference channel when joint decoding is replaced by successive decoding. When interference is weak, it is shown that, for a wide range of transmitters' powers, rate-splitting and joint decoding are not required. A single split scheme with successive decoding achieves the Han-Kobayashi sum-rate. When rate-splitting is required, a novel rate-splitting scheme is proposed that does not use joint decoding. The number of required splits and the amount of power allocated to each split are expressed in closed forms. It is proved that the difference between the sum-rate of this scheme and that of the Han-Kobayashi scheme is bounded, even when transmitters' powers approach infinity.
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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 | 0.000 | 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".