Method to suppress narrowband interference for OFDM radar
Bibliographic record
Abstract
Orthogonal frequency division multiplexing which is called OFDM for short is not only a popular modulation technique in communication systems but also a good method to generate radar signals. A joint radar and communication system could be realised by an OFDM system according to some off‐the‐shelf works. The radar functionality is mainly considered here, which requires the system to equip with the ability to suppress interference. The typical radar signal, frequency‐modulated continuous wave, can be viewed as narrowband interference for a large bandwidth OFDM radar with comparably short duration of OFDM symbols. Here, an interference suppression algorithm suitable for any type of narrowband interference is proposed for OFDM radar. The atomic norm minimisation (ANM) method involved in compressed sensing is introduced to obviate the interference. Then, the data with little interference can be reconstructed by reformulating the ANM as a semi‐definite program. Meanwhile, the level of noise is quelled effectively in terms of the atomic norm soft‐thresholding method and the gridless version of SPICE. Finally, the numerical simulation is performed to verify the effectiveness of the proposed method.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".