Model-Based Deep Learning Tuning of Reconfigurable Intelligent Surface for OFDM Radar Interference Mitigation
Bibliographic record
Abstract
This paper proposes a model-based deep learning framework for Reconfigurable Intelligent Surface (RIS)-aided Orthogonal Frequency-Division Multiplexing (OFDM) radar interference mitigation without any assumption on the transmitted waveform. First, a modified Multiple Signal Classification (MUSIC) algorithm generates coarse estimates of target and interferer angles. These estimates feed a lightweight MLP that jointly optimizes RIS amplitude–phase settings via a custom loss balancing SINR enhancement with angle-estimation fidelity. A convolutional notch filtering step then deepens the null at the interference direction, and a subcarrier-pooling strategy fuses per-frequency spectra to ensure uniform suppression across the band. Simulation results under automotive radar conditions demonstrate robust interference mitigation, high angular resolution, and precise angle recovery in dense environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".