Parallel-trellis turbo equalizers for sparse-coded transmission over sparse multipath channels
Bibliographic record
Abstract
A novel coding and turbo equalization scheme for sparse multipath channels is presented in this paper. A burst-error-correcting convolutional code, which coding and interleaving via a sparse encoder and a convolutional interleaver, is selected as the channel code. A previously-proposed parallel-trellis framework is adopted for implementing the maximum a posteriori (MAP) equalizer and decoder in the turbo equalizer. Such a system exhibits a similar level of performance to a conventional system using a random-error-correcting convolutional code together with a row /column (R/C) or random block interleaver. Yet, the proposed system has the advantages of low latency and low memory requirements when compared to those conventional systems. This is due to (i) the elimination of the block-type interleavers and deinterleavers and (ii) the parallelism achieved in the turbo equalizer. One disadvantage of the proposed system is to decrease in throughput, as more tail symbols are required for proper termination of all sub-trellises in the decoder. Extension of the proposed system to turbo codes is considered. Also, the effect of prefiltering on a nonminimum-phase, high definition television (HDTV) channel is examined. Results indicate that using the feedforward filter (FFF) of a nonuniformly-spaced decision feedback equalizer (NU-DFE) performs almost as good as using one with a large number of uniformly-spaced taps. However, far fewer computations are needed to find the optimum tap values of the sparse prefilter.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".