Adaptive Blind Equalization of Nonlinear Channels and Chaotic Systems using Coupled EKF and RLS Estimator
Why this work is in the frame
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Bibliographic record
Abstract
A novel approach, which employs coupled extended Kalman filter (EKF) and recursive least squares (RLS) estimator is proposed for adaptive blind equalization of nonlinear channels and chaotic systems. The conventional models of nonlinear FIR Volterra channels, nonlinear IIR channels, and chaotic communication systems are cast into regression models to formulate RLS algorithm to estimate the unknown channel parameters. The state-space representation of the channels have been formulated to develop the EKF to estimate the state from which the input sequence can be recovered. Then, the EKF and RLS estimator are coupled to estimate jointly the channel parameters and the state. The stability problem of the estimator is also addressed. The proposed estimator is corroborated with simulation examples on adaptive blind equalization of nonlinear FIR Volterra channels, nonlinear IIR channels, and chaotic communication systems. Simulation results show that the proposed estimator is effective in recovering the input sequence as well as the channel parameters blindly from the channel output measurement.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it