LS FFT-based channel estimators using pilot-embedded data-bearing approach in space-frequency coded MIMO-OFDM systems
Bibliographic record
Abstract
Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) is one prominent communication system for realizing high speed data transmission services. One critical issue for such systems is channel estimation. In this paper, we first develop a pilot-embedded data-bearing (PEDB) approach for joint channel estimation and data detection. Then we propose a least square (LS) FFT-based channel estimator by employing the concept of FFT-based channel estimation to improve the performance of the PEDB-LS channel estimation. Also, the effects of model mismatch error when considering non-integer multipath delay profiles, and its performance are investigated. We further propose an adaptive LS FFT-based channel estimator that employs the optimum number of significant taps. Simulation results reveal that the adaptive LS FFT-based estimator provides superior performance under quasi-static channels or low Doppler's shift regimes
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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) | 0.000 | 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 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".