Distributed Joint Source-Channel Coding Using Unequal Error Protection LDPC Codes
Bibliographic record
Abstract
This paper presents a general approach to designing distributed joint source channel (DJSC) codes with arbitrary rates for communication of a pair of correlated binary sources over noisy channels. In this approach, both distributed compression and channel error correction are simultaneously achieved by transmitting, for each source, a fraction of the information bits together with the parity bits of a systematic channel code. This approach is shown to be asymptotically optimal, i.e., any rate-pair in the achievable rate-region can be approached as the codeword length is increased. The practical realization of such a code requi res the design of a pair of channel codes with unequal error protection (UEP) properties determined by the inter-source correlation and the channel capacity available to each source. Towards this end, a linear programming based procedure for jointly optimizing the degree profiles of a pair of irregular LDPC codes to achieve the required UEP properties is presented. Experimental results obtained with both binary symmetric channels and binary-input Gaussian channels are presented, which demonstrate that the proposed UEP-DJSC codes can significantly outperform separate source-channel codes, as well as previously reported joint source-channel coding schemes, particularly for short codeword lengths.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".