Sample rejection for efficient simulation of intersymbol interference channels with MLSD
Bibliographic record
Abstract
A sample rejection scheme introduced previously is generalized for the simulation of multidimensional communication systems. We consider the use of sample rejection (a special case of importance sampling) for efficient simulation of uncoded continuous transmission with periodic trellis-termination over static intersymbol interference (ISI) channels and maximum likelihood sequence detection. Previously proposed rejection regions are applicable only for finite lattices with rectangular or circular symmetries and moderate dimensionality. However, these regions are not applicable or are inefficient if the dimensionality is increased for large block-lengths, or if the lattice symmetries are absent because of the ISI. Hence, we investigate sliding-window (near) maximum-likelihood sequence decoding (MLSD) to resolve the dimensionality problem. In particular, we study the truncated Viterbi algorithm and feedback decoding. We propose several modifications to these two algorithms using sample rejection principles to improve the simulation efficacy for the conventional Viterbi algorithm while achieving near MLSD performance. Finally, numerical examples confirm that feedback decoding and its modifications can be more efficient for simulations of ISI channels and near MLSD than the Viterbi algorithm.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".