Distributed Joint Source-Channel Coding Using Unequal Error Protection LDPC Codes
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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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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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