Low-Latency Source-Channel Coding for Fading Channels with Correlated Interference
Why this work is in the frame
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Bibliographic record
Abstract
We investigate the problem of sending a Gaussian source over a Rayleigh fading channel with Gaussian correlated interference known to the transmitter using low-latency codes. For the matched bandwidth case between the source and the channel, we show that among all single-letter codes, the uncoded scheme achieves the lowest mean square error distortion under full correlation between source and interference, and hence it is optimal. To benefit from nonlinear strategies for other scenarios, we derive the necessary conditions for optimality and propose an iterative algorithm based on joint optimization between the encoder and the decoder. A reduced-complexity approach for the implementation of the design algorithm is presented based on Monte-Carlo (at the encoder side) and importance sampling (at the decoder side) techniques. Furthermore, the scalability of our low-latency scheme is improved by modifying the search process at the encoder side using a targeted search method.
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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 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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