Error Exponents for Asymmetric Two-User Discrete Memoryless Source-Channel Coding Systems
Bibliographic record
Abstract
We study the transmission of two discrete memoryless correlated sources, consisting of a common and a private source, over a discrete memoryless multiterminal channel with two transmitters and two receivers. At the transmitter side, the common source is observed by both encoders but the private source can only be accessed by one encoder. At the receiver side, both decoders need to reconstruct the common source, but only one decoder needs to reconstruct the private source. We hence refer to this system by the asymmetric two-user source-channel coding system. We derive a universally achievable lossless joint source-channel coding (JSCC) error exponent pair for the two-user system by using a technique which generalizes Csiszar's type-packing lemma (1980) for the point-to-point (single-user) discrete memoryless source-channel system. We next investigate the largest convergence rate of asymptotic exponential decay of the system (overall) probability of erroneous transmission, i.e., the system JSCC error exponent. We obtain lower and upper bounds for the exponent. As a consequence, we establish a JSCC theorem with single-letter characterization and we show that the separation principle holds for the asymmetric two-user scenario. By introducing common randomization, we also provide a formula for the tandem (separate) source-channel coding error exponent. Numerical examples show that for a large class of systems consisting of two correlated sources and an asymmetric multiple-access channel with additive noise, the JSCC error exponent considerably outperforms the corresponding tandem coding error exponent.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".