A Low Complexity Frequency Domain Iterative Decision-Directed Channel Estimation Technique for Single-Carrier Systems
Bibliographic record
Abstract
A low complexity frequency domain iterative decision-directed channel estimation (FD-IDDCE) technique for single-carrier (SC) systems with frequency domain multiplexed (FDM) pilots is proposed. The tentative hard decisions from either the frequency domain equalizer output or the Viterbi decoder output are used as extra pilots to further improve the initial frequency channel estimates using a cascaded 2 times 1D Wiener filter. The issue of noise enhancement when finding the least square (LS) estimates of the channel frequency response (CFR) using the tentative decisions in the frequency domain is overcome by the so called frequency replacement algorithm, which replaces the noise enhanced LS estimates of the CFR with the corresponding channel frequency estimates in the previous iteration, by comparing with a threshold. Using the proposed FD-IDDCE, the frame error rate (FER) performance for coded SC systems using FDM pilots with frequency expanding technique and frequency domain superimposed pilot technique was found to be about 0.4 dB and about 1 dB away from the FER performance with known CFR at FER=10-2.
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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 | 0.001 | 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".