Optimizing Pulse Shaping Filter for DOCSIS Systems
Bibliographic record
Abstract
This paper proposes a cost efficient nearly linear phase approximation to an square root raised cosine (SRRC) pulse shaping filter that satisfies the out-of-band spectral constraints of DOCSIS down-stream channels. A nearly linear phase filter structure is converted to an SRRC filter using a weighted and sampled least-squares criterion to fit the magnitude and phase responses. To ensure stability, the search for optimum coefficients is constrained using the Steigliz-McBride (SM) and Gauss-Newton (GN) methods, which unfortunately also eliminates sets of stable coefficients, one of which could be and probably is the optimum. To expand the sets of coefficients that produce stable filters in the SM and GN methods, the transfer function is parameterized in a special way. The effectiveness of the proposed filter is verified and compared with other approaches.
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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 | 0.001 | 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 | 0.001 |
| 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".