Novel design of microwave photonic transceivers for communication, radar, and surveillance systems on chip (Conference Presentation)
Bibliographic record
Abstract
Dear Technical Committee, Prof. Josè Azaña and I have gladly accepted the invitation by Dr. Pavel Cheben to present an Invited Talk at the Integrated Optics: Design, Devices, Systems and Applications of the SPIE Optics and Optoelectronics Symposium. Best regards, Daniel Onori Abstract: The key goal for next-generation RF signal transceivers for communication, radar, and surveillance systems is a chip-scale implementation able to provide the highest performance in terms of total frequency range of operation (i.e., from 0.5 to 40 GHz and beyond), dynamic range, and linearity. Unfortunately, microwave technology is revealing unable to achieve the target performance with the desired level of compactness. In fact, its intrinsic bandwidth constraints impose the need of components that prevent a chip-scale integration, such as RF filter banks and multiple crystal oscillators. The generation and detection of RF signals through photonic coherent architectures results extremely attractive due to the promising wide bandwidth and large tunability that could be achieved with these technologies. When implemented in integrated-waveguide formats, photonic devices also present significantly reduced footprint with respect to conventional RF components. However, in order to reduce the interference noise introduced by optical sources exploited in the schemes, current solutions rely on technologies or components that prevent a monolithic on-chip integration. For instance, self-heterodyning schemes use tunable RF synthesizers for the electro-optical generation of the required coherent optical tones, while injection locking techniques, used to cancel the interference noise between the optical sources, stem from optical circulators, that cannot be integrated on chip. In this talk, we will review recent work on a novel noise cancelling architecture used to suppress the interference noise introduced by the lasers that feed the system and preserve the integrity of the processed signals during the operation. The solution overcomes the mentioned main drawbacks of the previously proposed scheme, enabling the realization of RF transceivers with high-performance and reduced footprint. Considering the commercial and integration potential of silicon photonics technology, we will discuss the advantages of a chip-scale implementation of this new design in terms of performance, reliability, and cost.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".