Robust Deep Joint Source Channel Coding with Time-Varying Noise
Bibliographic record
Abstract
Deep Joint Source-Channel Coding (JSCC) has gained increased attention, asserting its significance in the communication field. However, existing Deep JSCC techniques struggle to mitigate time-varying noise due to the deep neural networks being trained beforehand and fixed. To address this issue, we propose a robust deep JSCC scheme. Firstly, a multi-network parallel structure, as well as error-correcting codes, is introduced to effectively exploit label information. Secondly, a closed-form linear encoder and decoder pair is employed at the input and output ends of the channel to deal with the varying noise, which releases the neural network from dealing with a large range of varying noise levels. Thirdly, a transfer learning algorithm is utilized for estimating real-time noise statistics, which outperforms conventional estimation methods when noise statistics are time-dependent. These three components are effectively integrated as a comprehensive transmission system. Experimental results demonstrate that our optimized scheme outperforms existing approaches in the literature.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".