Bi-directional soft-output M-algorithm for iterative decoding
Bibliographic record
Abstract
A method to produce soft-outputs is proposed for the M-algorithm. The soft-output M-algorithm (SOMA) reduces the complexity of trellis decoding by retaining only M states per trellis depth. Its complexity increases with M rather than with the number of states in the trellis. We also propose an improved SOMA that is based on bi-directional decoding. The performance of the SOMA and the bi-directional SOMA (bi-SOMA) are assessed in decoding a turbo code and in turbo equalization. Simulation results show negligible performance loss when a 16-state turbo code is decoded by the SOMA with M = 12. For turbo equalization, near-optimal performance can be achieved by retaining only a small number of equalizer states as long as the. number of states retained by the decoder is sufficiently large. For a BPSK turbo equalization system with 16 states in both trellises, a SOMA-equalizer with M = 4 and a bi-SOMA decoder with M = 8 suffices. For a QPSK system with 256 equalizer states and 16 decoder states, a SOMA-equalizer with M = 16 and a SOMA-decoder with M = 12 suffices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".