Rate Splitting and Successive Decoding for Gaussian Interference Channels
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Bibliographic record
Abstract
Most coding schemes proposed for the interference channel take advantage of joint decoding to enlarge 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. First, the strong interference class is examined, and it is proved that if transmitters' powers satisfy certain conditions, successive decoding is optimal and achieves the sum-capacity. The number of the required splits, the amount of power allocated to each split, and the order of decoding at receivers are explicitly determined. Second, the weak interference class is examined. 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 shown that, for a wide range of transmitters' powers, this scheme achieves the sum-rate of the Gaussian Han-Kobayashi scheme. Moreover, it is proved that the difference between the sum-rate of this scheme and that of the Gaussian Han-Kobayashi scheme is bounded, for all values of transmitters' powers.
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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 | 0.001 |
| 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 it