Joint Source–Channel Coding Excess Distortion Exponent for Some Memoryless Continuous-Alphabet Systems
Bibliographic record
Abstract
We investigate the joint source-channel coding (JSCC) excess distortion exponentEJ(the exponent of the probability of exceeding a prescribed distortion level) for some memoryless communication systems with continuous alphabets. We first establish upper and lower bounds forEJfor systems consisting of a memoryless Gaussian source under the squared-error distortion fidelity criterion and a memoryless additive Gaussian noise channel with a quadratic power constraint at the channel input. A necessary and sufficient condition for which the two bounds coincide is provided, thus exactly determining the exponent. This condition is observed to hold for a wide range of source-channel parameters. As an application, we study the advantage in terms of the excess distortion exponent of JSCC over traditional tandem (separate) coding for Gaussian systems. A formula for the tandem exponent is derived in terms of the Gaussian source and Gaussian channel exponents, and numerical results show that JSCC often substantially outperforms tandem coding. The problem of transmitting memoryless Laplacian sources over the Gaussian channel under the magnitude-error distortion is also carried out. Finally, we establish a lower bound forEJfor a certain class of continuous source-channel pairs when the distortion measure is a metric.
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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 | 0.014 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".