A Downscaled Faster-RCNN Framework for Signal Detection and Time-Frequency Localization in Wideband RF Systems
Bibliographic record
Abstract
We propose a wideband spectrum sensing technique to detect and localize wireless radio frequency (RF) signals of interest in time and frequency when uninteresting signals cause RF interference (RFI). Specifically, we adopt and downscale the existing Faster-RCNN (FRCNN) framework to achieve better signal detection and localization than the state-of-the-art. For experimental evaluation, we present a data generation framework for Wi-Fi as the signals of interest and the Bluetooth and microwave oven signals as the RFI. Experiments reveal that (i) the downscaled FRCNN model can achieve up to a mean average precision (mAP) of 0.8, significantly outperforming the state-of-the-art, (ii) feature extraction with the VGG-13 architecture gives the best mAP with pretrained weights and configured as trainable, (iii) for signal detection in real RF traces, when compared to training purely with synthetic RF data, a better mAP can be achieved by training with a mixture of synthetic and real RF traces or by fine tuning the synthetically-trained weights with an additional round of training on a small amount of real RF traces, and (iv) the mAP performance decreases as the signal to noise ratio (SNR) is lowered.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".