Iterative parallel-trellis MAP equalizers with nonuniformly-spaced prefilters for sparse multipath channels
Bibliographic record
Abstract
A maximum a posteriori (MAP) equalizer is derived in this paper by formulating the forward/backward recursion MAP algorithm (i.e. the BCJR algorithm) on a parallel-trellis representation that was previously proposed for equalizing sparse multipath channels. Several enhancements are suggested to further improve the performance and/or reduce the complexity of the resultant MAP equalizer. Results show that the parallel-trellis MAP equalizers utilizing 2-state trellises are the predominant structures applicable to minimum-phase sparse multipath channels when binary phase shift keying (BPSK) modulation is assumed. However, for nonminimum-phase sparse multipath channels, prefiltering is generally indispensable and is accomplished using the feedforward filter (FFF) of a nonuniformly-spaced decision feedback equalizer (NU-DFE) optimized under the minimum mean square error (MMSE) criterion, which also preserves the sparseness of the resultant minimum-phase impulse response. The simplicity of the 2-state, parallel-trellis structure leads to a low computational load that is comparable to those of uniformly-spaced and nonuniformly-spaced tapped-delay-line (TDL) equalizers, but attains much superior performance over the latter types of equalizers. Moreover, iterations can be added to improve the accuracy of the inter-trellis intersymbol interference (ISI) estimates and the residual ISI estimates due to channel truncation, and are especially effective in mitigating the precursor ISI terms due to non-ideal prefiltering.
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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".