Coupling Data Transmission for Multiple-Access Communications
Bibliographic record
Abstract
We consider a signaling format where the information to be communicated from a single or multiple transmitters to a receiver is modulated via a superposition of independent data streams. Each data stream is formed by error-correction encoding, constellation mapping, replication and permutation of symbols, and application of signature sequences. The relations between the data bits and modulation symbols transmitted over the channel can be represented by a sparse graph. In the case where the modulated data streams are transmitted with time offsets the receiver observes spatial coupling of the individual graphs into a graph chain enabling efficient demodulation/decoding. We prove that a two-stage demodulation/decoding method, in which iterative demodulation based on symbol estimation and interference cancellation is followed by parallel error-correction decoding, achieves capacity on the additive white Gaussian noise channel asymptotically. We compare the performance of the two-stage receiver to the receiver which utilizes hard-decision decoding feedback between the error-correction decoders and the iterative demodulator and estimate the gap between the achievable spectral efficiency and the channel capacity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".