Direct RF sampling transceiver architecture applied to VHF radio, ACARS and ELTs
Bibliographic record
Abstract
Along with the development of aviation industry, there is a rising demand for a breakthrough in avionic systems. The future avionics, besides advancing the current performance and security level, also need to increase the efficiency in size, weight, power and cost (SWaP-C) constraints. Among different solutions, Direct RF Sampling (DRFS) architecture is considered as one of the most promising ones, offering the benefits of hardware simplicity, Integrated Modular Avionic (IMA) and multi-system architecture compatibility. The objective of this paper is to present the new development and implementation of this innovative architecture in both transmission and reception mode. Targeting at some of the most crucial communication systems in VHF avionic bands, including VHF Radio, Aircraft Communication and Address Reporting System (ACARS), and Emergency Locator Transmitter (ELT), this paper describes an approach to create the Signal of Interest (SOI) (transmission) and to process the received signal (reception) in Direct RF, without the LO mixer as in conventional architecture. In addition, in order to demonstrate the advantages of DRFS in future avionics, the paper introduces a solution to improve the coverage and detecting ability of ELT signals. By integrating a spectrum scanner in FPGA, running independently and in parallel with the others avionics, the implementation of this system costs nothing but some FPGA resources, yet reliable and robust. The results show that the DRFS transceiver architecture meets the standards of the regarding avionics (VHF radio, ACARS and ELT). Furthermore, the ELT Detector in FPGA not only can separate the analog ELT signal from other interferences, but also has the sensitivity as good as −100 dBm.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".