Bibliographic record
Abstract
A new blind channel estimation technique is presented for uncoded orthogonal frequency-division multiplexing (OFDM) systems. Instead of using pilots to sound the channel, a decision algorithm first makes primary estimates of the data symbol for each subcarrier based on a constrained linear minimum mean square error (MMSE) criterion. Then, these estimates are applied to optimal MMSE channel estimation. The technique requires only one value from the time–frequency correlation of the channel transfer function. Performance is evaluated by simulation so that comparison can be made with known optimal coherent/differential detection. Compared with known decision-directed Kalman-based estimation and two pilot-aided OFDM schemes (block pilots and comb pilots), the presented technique performs better for regions with mid to high signal-to-noise ratios (SNRs). Its robustness to the time variation of the channel is also quantified by simulation, showing only small degradation in performance relative to the quasistatic case of wireless local area network (WLAN) systems. Finally, the impact of covariance assumptions in the channel modeling is quantified using simulation, offering a feel for the performance with mismatch between the channel model and the receiver assumptions.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".