Turbo equalization using frequency-domain shortening filter for broadband single-carrier transmission over frequency-selective fading channels
Bibliographic record
Abstract
The paper presents a combined scheme using a frequency-domain shortening filter and turbo equalization for broadband single-carrier QAM transmission over frequency-selective fading channels. The frequency-selective fading channel response is first converted to a shortened desired channel impulse response by a frequency-domain pre-filter (FDPE). The shortened desired channel impulse response (SCIR) and convolutional code are used in concatenation for iterative equalization/decoding. The pre-selected small number of taps of the shortened desired channel impulse response helps to reduce the complexity of the iterative equalization/decoding. The optimum selection of coefficients/taps of the frequency-domain pre-filter and desired channel impulse response to achieve the minimum mean-square error (MMSE) is discussed. Simulation results on the performance of the proposed scheme in various frequency-selective fading channels indicate a substantial performance gain as compared to other high-performance equalization/detection schemes.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".