Parallel-Trellis Turbo Equalizers for Sparse-Coded Transmission over SISO and MIMO Sparse Multipath Channels
Bibliographic record
Abstract
This paper describes a low-complexity turbo equalizer for coded sparse multipath channels whose length can span hundreds of symbol intervals. First, for single-input-single-output (SISO) systems, an existing parallel-trellis framework, which consists of a bank of identical regular trellises, is exploited to construct the maximum a posteriori (MAP) equalizer in the turbo equalizer. This MAP equalizer, when combined with prefiltering, can equalize a broad selection of sparse multipath channels, including those with nonminimum phase, by using only M-state trellises, where M is the constellation size. For multiple-input-multiple-output (MIMO) systems, a MIMO prefilter and a bank of M-state parallel-trellis MAP equalizers are deployed according to a layering structure. The total number of states needed is only NTM, where NTis the number of transmit antennae. For both SISO and MIMO systems, a class of binary convolutional and turbo codes having sparse generator polynomials are chosen as the coding schemes. These codes partially integrate channel interleaving with encoding, which allows a simple channel interleaver with low latency and memory storage requirement to be employed. The MAP decoders for these codes can also be implemented with the same parallel-trellis framework as the MAP equalizers. Overall, parallel processing is supported throughout the turbo equalizers. The amount of computational time reduction when compared to a turbo equalizer using a single-trellis MAP equalizer and decoder is proportional to the number of trellises in the parallel-trellis MAP equalizer and decoder. The performance of the parallel-trellis turbo equalizers is evaluated on static and Rayleigh fading sparse multipath channels via Monte Carlo simulations
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".