Hybrid Digital-Analog Coding of Memoryless Gaussian Sources over AWGN Channels with Bandwidth Compression
Bibliographic record
Abstract
We present a low-complexity and low-delay joint source-channel coding method for bandwidth compression using a hybrid digital-analog (HDA) coding/modulation system based on the recent work in Skoglund, M et al., (2005), Analytical optimal distortion expressions (under both matched and mismatched channel conditions) are obtained for the proposed HDA system with a linear analog part for a memoryless Gaussian source and additive white Gaussian noise (AWGN) channel under the mean squared error distortion measure. We consider two HDA coding schemes, both of which employ a vector quantizer cascaded with binary phase-shift keying (BPSK) modulation in the digital part, but differ in that they use linear (resp. non-linear) coding with pulse amplitude modulation (PAM) in the analog part. We derive an optimal power allocation scheme for the system with linear analog coding and present performance comparisons with purely analog and purely digital systems. Simulation results show that, under linear analog coding, the proposed scheme outperforms the medium to high channel signal-to-noise ratios (CSNRs). Furthermore, the performance of the HDA scheme with the linear analog part is within 1 dB of the optimal distortion bound for the mismatched HDA system for high CSNRs; for the scheme with non-linear analog coding, the performance can be improved at high CSNRs
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".