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 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".