Sample Rejection for Efficient Simulation of Binary Coding Schemes Over Quantized Additive White Gaussian Noise Channels
Bibliographic record
Abstract
We re-examine sample rejection (SR), introduced previously as an easy-to-implement efficient simulation technique. Since the decoding operation often represents a major part of the required simulation time, SR can be used to avoid decoding of the received sequences that are known beforehand to be decoded error-free. Previous work seems to indicate that SR may be effective only for simulations having small dimensionality, less than 10. We assume estimation of decoded bit-error probabilities for a general coding scheme of finite block length transmitted over an additive white Gaussian noise channel with quantized output using binary antipodal signaling and maximum-likelihood sequence decoding. We show that knowledge of the minimum Hamming distance of the code and conditioning on the transmitted sequence can be exploited to form the rejection regions. In particular, we investigate hypersphere, hypercube, and hyperquadrant rejection regions. Our analysis shows that SR can be effective for some systems with dimensionality on the order of hundreds with soft-decision decoding, and some systems with dimensionality more than a thousand with hard-decision decoding if the rejection regions are properly chosen.
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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 | 0.000 |
| Open science | 0.001 | 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".