Highly Reconfigurable Narrowband Microwave Photonic Filter Based on Chirp-Like Sliced ASE Source
Bibliographic record
Abstract
Microwave filters are essential for front-end receivers in applications such as radar, radio over fiber (RoF), and sensing. However, conventional microwave filters implemented by electronic approaches face limitations such as susceptibility to electromagnetic interference (EMI), restricted tuning flexibility, and challenges at high frequencies, whereas microwave photonic filters (MPFs) offer a solution to those limitations. In this article, we use a chirp-like sliced amplified spontaneous emission (ASE) source to achieve an MPF that exhibits an ultranarrow bandwidth, wide frequency tunability, reliable frequency stability, and high reconfigurability. The ultranarrowband performance is achieved using a chirp-like sliced ASE source to compensate for the nonuniformity of the delay-line taps caused by third-order dispersion (TOD) in optical fibers. By chirping the sliced ASE source in the opposite direction of the TOD-induced chirp, we ensure uniformly spaced delay-line taps across a 40-nm ASE bandwidth when a 25-km single-mode fiber (SMF) is used, resulting in an ultranarrow passband of 43 MHz with the tuning range from 85 MHz to 9.4 GHz. In addition, our MPF exhibits excellent frequency stability, with frequency drifts remaining below 250 kHz at 9.4 GHz over 60 min. Finally, by incorporating a programmable optical filter, the system allows seamless switching between single-passband, dual-passband, and multipassband modes without altering the experimental setup. Each passband’s center frequency can be independently tuned while preserving ultranarrowband performance, demonstrating unprecedented levels of reconfigurability and versatility. We believe our proposed MPF will play a key role in modern radio frequency (RF) systems for next-generation radar and high data rate wireless communication.
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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 | 0.000 |
| 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 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".