Blind channel estimation for equalisation in dispersive fading channel
Bibliographic record
Abstract
A new blind channel estimation technique is presented for non-linear/linear equalisation in a frequency-selective Rayleigh fading channel. At each symbol interval, a decision algorithm first makes a primary data estimate based on constrained linear minimum mean square error criterion, and then this is applied to subsequent channel estimation. Channel estimates are obtained in the form of two alternative, related methods: directly from the Wiener solution or Kalman-based recursion. The former performs better but requires more values from the normalised time-correlation function. The performance is evaluated by simulation, allowing fair comparison with the benchmark of equalised coherent detection; optimal uncoded orthogonal frequency division multiplexing with perfect channel state information at the receiver; the conventional decision-directed Kalman filtering which employs channel tracking with delay; and for the special case of flat fading, an optimised, pilot symbol-assisted modulation system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".