Design of nonuniformly-spaced tapped-delay-line equalizers for sparse multipath channels
Bibliographic record
Abstract
The problem of designing nonuniformly-spaced tapped-delay-tine equalizers (NU-E) for sparse multipath channels is addressed. First, analytical expressions that explicitly indicate the tap positions and tap values of the infinite-length, T-spaced linear equalizers (LE) and decision feedback equalizers (DFE) under the zero-forcing (ZF) and minimum mean square error (MMSE) criteria are derived using the conventional matched receive filter (MF) and a square root raised cosine (SRRC) receive filter that is matched to a SRRC transmit filter. For both receive filter systems, the ZF-LE and the MMSE-LE for sparse multipath channels are nonuniformly-spaced with identical tap positions, but the ZF-DFE and MMSE-DFE are uniformly-spaced. Next, two suboptimum tap allocation algorithms based on the positions of large magnitude taps in the infinite-length, T-spaced equalizers are proposed for designing finite-length, T- and T/2-spaced MMSE NU-LE and NU-DFE. Results have shown that the proposed NU-E exhibit superior performance over uniformly-spaced tapped-delay-line equalizers (U-E) for the same number of taps, and only a small loss in SNR when compared to U-E with a large number of taps. Moreover, the SRRC receive filter is found to be a better front-end receive filter than the MF when used together with an appropriately-designed NU-E. A simple method that assigns extra T/2-spaced taps to improve the timing insensitivity of the proposed NU-E is also included.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".