Reduced-search trellis decoding of coded modulations over ISI channels
Bibliographic record
Abstract
Optimal Viterbi decoding of trellis codes transmitted over channels exhibiting long intersymbol interference (ISI) can be computationally infeasible. Suboptimal trellis search schemes promise much better general performance than decision feedback equalization (DFE) or linear equalization, M. Eyuboglu and S. Qureshi's (1989) reduced-state sequence estimation (RSSE) is the most general. It is shown that two reduced-search breadth-first trellis decoders, namely the M-algorithm and the newer T-algorithm, can provide performance equivalent to RSSE, but at a substantially reduced computational load. This advantage is especially pronounced for (minimum-phase) ISI responses with precursors. With the M or T-algorithms, a more powerful trellis code can be used to achieve a better error rate. These decoders operate on the original trellis without the need for the partitioning required by RSSE.>
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".