Schroeder sequences for time dispersive frequency selective channel estimation using DFT and Least Sum of Squared Errors methods
Bibliographic record
Abstract
Digital communication systems operating on time varying depressive channels often employ a signalling format in which customer data are organized in blocks proceeded by a known sequence. The training sequence at the beginning of each block is used to train an adaptive equalizer and/or data sequence detector to combat intersymbol interference (ISI). This paper addresses the problem of comparing the Schroeder sequences as a very close to optimal training sequence for channel estimation (start up) in communication systems over time dispersive frequency selective channels. Schroeder sequences of comparable lengths to the designed -computer searched- sequences demonstrated a tight performance for both the optimal sequences designed using Discrete Fourier Transform (DFT) technique and the sequences designed via Least Sum of Squared Errors (LSSE) channel estimation. Performance results are provided for Schroeder sequences of lengths 36 and 28 (the choice of 28 is driven by the fact that channel estimation sequences for GSM system are of length 28).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".