Optimal rate profile for multi‐user multi‐rate transmission systems by bivariate fixed‐point analysis
Bibliographic record
Abstract
A K ‐user multi‐rate code is proposed for a Gaussian multiple access channel with binary inputs, equal‐power, and symbol synchronisation. In this multi‐rate transmission, K users are equally divided into M groups. For each user in the m th group, a rate‐ regular repeat‐accumulate code serially concatenated with a length‐ spreading is employed. The transmitted rate of each user in the m th group is . At the receiver, iterative joint decoding (IJD) and hybrid interference cancellation (HIC) schemes are considered. For each decoding scheme, a bivariate fixed‐point analysis is applied to explicitly represent as a function of mutual information outputs. On the basis of these basic explicit representations, a united unreliable region is given, where users in at least one group are undecodable. The complementary set of the united unreliable region gives an optimal rate profile that achieves the maximum sum rate. Numerical results show that, for the IJD scheme with M increments, the maximum sum rate increases, approaches the Shannon limit, and exceeds that in conventional equal rate transmission. The maximum sum rate of the HIC scheme, which provides much lower decoding complexity than the IJD scheme, is superior to the conventional successive interference cancellation scheme.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".