Neural network modeling and identification of nonlinear MIMO channels
Why this work is in the frame
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Bibliographic record
Abstract
The paper proposes a neural network (NN) approach for modeling and identification of a class of nonlinear multiple-input multiple-output (MIMO) channels. The unknown MIMO system is composed of a set of single-input memoryless nonlinearities followed by a linear combiner. The proposed NN model consists of a set of single-input memoryless NN blocks followed by an adaptive linear combiner. The performance of the proposed scheme is shown to outperform the classical multi-layer perceptron (MLP) in terms of convergence speed, mean squared error (MSE) and computational complexity. For uncorrelated inputs, the proposed NN structure enables the identification of each of the unknown nonlinearities as well as the combining matrix. Several simulation results and applications are presented in the paper, including tracking of slowly time-varying MIMO channels, and fault detection and characterization in nonlinear MIMO systems.
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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 | 0.001 | 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 | 0.000 | 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