A Novel Approach to Control the Sidelobe Levels in Orthogonal Frequency Division Multiplexing Radar Waveform Design Using Broyden-Fletcher-Goldfarb-Shanno Algorithm
Bibliographic record
Abstract
Employing orthogonal frequency division multiplexing (OFDM) for radar applications has attracted many researchers in recent days. In OFDM systems the reduction of out-of-band (OOB) radiations is one of the most discussed and researched topics. To successfully design an OFDM based overlay system, it is necessary to reduce the sidelobe levels in OFDM signals. In this paper a novel technique for reducing the sidelobe in OFDM radar signals is projected and examined. Subcarrier weighting technique is the method used to scale down the sidelobe heights by multiplying real valued weighting coefficients with the used subcarriers. In order to obtain optimal subcarrier weights a numerical optimization technique called Broyden-Fletcher-Goldfarb-Shanno (BFGS) is utilized. The proposed scheme applies BFGS method to enhance the performance of OFDM radar signal. The reduction in sidelobe levels thus obtained from the proposed method shows the superiority in functioning (small sidelobe crest and good resolution) shown with extensive simulation results.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".