RFNet: Fast and efficient neural network for modulation classification of radio frequency signals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automatic Modulation Classification (AMC) is a well-known problem in the Radio Frequency (RF) domain. Solving this problem requires determining the modulation of an RF signal. Once the modulation is determined, the signal could be demodulated making it possible to analyse the signal for various purposes. Deep Neural Networks (DNNs) have recently proven to be successful in solving this problem efficiently. However, since deep networks consist of several layers resulting in a high number of trainable parameters, the hardware implementations of these solutions are resource-demanding. In order to address this challenge, we propose an efficient deep neural network referred to as RFNet to tackle the AMC problem efficiently. This network introduces the novel Multiscale Convolutional (MSC) layer to extract robust features in different resolutions. In addition, the network takes advantage of several Separable Convolution Blocks (SCB). These blocks employ pointwise and depth-wise convolutions to reduce network complexity. We further introduce RFNet+ and RFNet++ as extensions of RFNet with fewer number of parameters. These variants include fewer floating-point operations and hence a lower hardware implementation cost. Experimental results using the challenging RadioML 2018 dataset show that RFNet-32++ achieves an average classification accuracy of 56.09% over all Signal-to-Noise Ratios (SNRs) and an accuracy of 92.21% in+20dB SNR using only 3.1K parameters. The small number of parameters makes the RFNet family a promising solution for future AMC systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 it