A Decision Directed Square-Root Free Inverse QR-Decomposition Based Groupwise Recursive Channel Estimator for SFBC-OFDM Systems
Bibliographic record
Abstract
In this paper, a decision directed square-root free inverse QR-decomposition based groupwise recursive (SIQR-GR) channel estimation scheme is proposed for space-frequency block coded orthogonal frequency division multiplexing (SFBC-OFDM) systems. The proposed SIQR-GR channel estimation algorithm employs sequences of scaled Givens rotations to recursively processes data decisions corresponding to the SFBC groups within each OFDM block. As a result of the groupwise recursive processing, the proposed SIQR-GR scheme yields improved system performance under doubly-selective (i.e., frequency- selective and fast fading) channels. To evaluate the performance advantages of SIQR-GR we compare the normalized mean square error (NMSE) and symbol error rate (SER) performances of SIQR-GR to those corresponding to previously proposed channel estimation schemes. Furthermore, we also present computational complexity comparisons between SIQR-GR and the previously proposed schemes. These comparisons show an excellent performance-complexity tradeoff achieved by SIQR-GR over the previously proposed solutions.
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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 | 0.002 |
| 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 | 0.000 | 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".