On Transmission of Multiresolution Gaussian Sources over Noisy Relay Networks
Bibliographic record
Abstract
This paper investigates joint source-channel coding (JSCC) in a decode-and-forward three-node relay network, in which scalable source coding (SSC) is coupled with superposition coding (SPC) to form a layered coding architecture of SSC-SPC. In contrast to any previously reported research using asymptotic capacity-based distortion (CBD) measure, we derive the mean-squared error end-to-end distortion (EED) of such JSCC system based on a real-valued Gaussian source, aiming to achieve better precision and practicality for applications in which channels are subject to large error probabilities. The EED evaluation is formulated and applied to demonstrate achievable gains of the SSC-SPC architecture versus a number of conventional approaches. Power allocation optimization is performed based on the developed non-asymptotic EED model and compared to that by using an asymptotic CBD measure in which symbol losses caused by channel error cannot be considered. We demonstrate the performance gaps between results solved from EED versus CBD in our numerical example, and conclude that the optimization using CBD behaves awkwardly in computing proper power allocation configurations in the considered SSC-SPC architecture.
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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 | 0.000 | 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 | 0.003 | 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 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".