Time-varying Microwave Photonic Filter over 46-GHz Bandwidth with High Tuning Speed
Bibliographic record
Abstract
Time-varying (TV) filters, with the ability to precisely process non-stationary high-speed microwave signals, are fundamental building blocks in wireless communications, Radar/Lidar systems, advanced metrology, etc. [1]–[3]. To date, it remains challenging to achieve time-varying microwave filters with fully reconfigurable spectral transfer functions, large tuning bandwidth (>10GHz), and high tuning speed (into the GHz range) simultaneously. This set of specifications are particularly interesting for applications in 5G/6G and cognitive communications. For example, digital TV filters can achieve hyperfine frequency resolution and high reconfigurability; however, their real-time processing bandwidth is inherently limited to below a few hundreds of MHz [4], [5]. Recently, significant efforts have been devoted to the development of TV microwave filters through the microwave photonics (MWP) approach, so-called MWP filters (MWPFs), as these have been shown to enable broad operation bandwidth, fast tuning speed and an important degree of flexibility. However, current TV MPFs still fall short of the set of specifications defined above, being particularly limited in regards to their versatility and tuning speed [6], [7]. For example, a notable level of reconfigurability can be achieved by cascading an optical frequency comb source and a programmable optical filter, but the reconfiguration speed is still limited by the tuning speed$(\sim$kHz) of the programmable optical filter [6]. On the other hand, a tuning speed of$\sim 1$GHz has been demonstrated using other approaches, but in these solutions the degree of reconfigurability is still fairly limited [7].
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".