Low and high-delay source-channel coding with bandwidth expansion and correlated interference
Bibliographic record
Abstract
We consider the problem of sending a Gaussian source over an additive white Gaussian noise channel with Gaussian correlated interference known to the transmitter. We study both low-delay and asymptotically high-delay (in the sense of infinite source and coding block lengths) joint source-channel coding schemes based on purely analog and hybrid-digital analog (HDA) schemes with bandwidth expansion, respectively. The achievable (square error) distortion region of these schemes under matched and mismatched noise power is analyzed. The low-delay scheme uses a non-parametric analog mapping that is designed using a joint optimization of the encoder and the decoder. Numerical results show that the non-parametric approach adapts better to the interference than the classical linear scheme. For the high-delay regime, we establish a lower bound on the system's distortion and propose a layered HDA scheme based on Wyner-Ziv and HDA Costa coding. The proposed HDA scheme is shown to perform close to the derived bound and to be resilient under noise mismatch.
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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".