A Trellis-Coded Quantization Approach to Transmitting Correlated Gaussian Sources Over a Fading MAC Without Transmitter-CSI
Bibliographic record
Abstract
It is known that source-channel separation is sub-optimal for communicating correlated Gaussian sources over a Gaussian multiple access channel (GMAC). Considering a two-to-one GMAC which undergoes Rayleigh block-fading, we present a novel approach to practical joint source-channel coding for the scenario in which the common receiver has instantaneous CSI but only the CSI distribution is available to the individual transmitters. This approach, referred to as source-channel trellis-coded vector quantization (SC-TCVQ), simply relies on using TCVQs as fixed-rate source-channel encoders. One key issue is the optimization of the TCVQ codebooks to the mean channel signal-to-noise ratio (CSNR), and to this end, we present an analytical method to obtain the rates required for codebook design. Another key issue is the joint estimation of the sources at the receiver, for which we present a detector-estimator based on the Cartesian product of the two encoder-trellises. Simulation results show that the proposed SC-TCVQ codes, in some cases, can even beat the asymptotic performance bound for a separate source-channel code consisting of a distributed vector quantizer and capacity achieving channel codes. SC-TCVQ appears to be the best known practical code design to date for the given communication problem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".