Source-Interference Recovery Over Broadcast Channels: Asymptotic Bounds and Analog Codes
Bibliographic record
Abstract
We consider the problem of joint recovery of a bivariate Gaussian source and of interference over the two-user Gaussian degraded broadcast channel in the presence of common interference. The interference, that is available non-causally at the encoder, is assumed to be Gaussian and correlated to the sources. The tradeoff between the distortion of the sources and the interference estimation error is studied; information-theoretic outer and inner bounds based on ideas from rate-distortion theory and hybrid coding are derived, respectively. More precisely, the outer bound is found by assuming additional knowledge at each user; the inner bound, however, is obtained by analyzing the distortion of a layered hybrid scheme based on proper power splitting, Costa and Wyner-Ziv coding. Low delay and complexity coding schemes based on analog mapping are next proposed. More specifically, parametric mappings based on linear and sawtooth curves are studied and optimized by minimizing an upper bound on the system's distortion; nonparametric mappings based on joint optimization between the encoder and the decoder using an iterative algorithm are designed. Numerical results show that for the special cases that are previously considered by Abou Saleh et al. (with no fading), the derived outer bound is tighter and the proposed hybrid scheme has a lower complex structure with no loss in performance. In addition, the proposed low delay nonlinear schemes outperform the linear scheme and perform relatively close to the inner bound under certain system settings.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".