Source Side Information Can Increase the Joint Source-Channel Coding Error Exponent
Bibliographic record
Abstract
We study the joint source-channel coding (JSCC) error exponent for discrete memoryless source-channel systems with side information which is correlated to the transmitted source. Two cases are considered: (1) the side information is available only at the decoder; (2) the side information is available at both the encoder and decoder. We employ the method of types to establish a lower bound for the JSCC error exponent for each case. As a consequence, a JSCC theorem on the reliable transmissibility of the source over the channel is obtained. It is noted that the same JSCC theorem applies for both cases. For binary sources and symmetric channels, we derive a sufficient condition for which the side information at the decoder can strictly improve the JSCC error exponent. Numerical results show that side information can enlarge the region for reliable transmissibility and increase the JSCC error exponent for a wide class of source-channel parameters.
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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 | 0.001 | 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".