Spectrum Sensing for Modulated Radio Signals Using Deep Temporal Convolutional Networks
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
Detecting the presence of radio signals in noise has been a decades-long problem in the signal processing domain. More recently, there has been a resurgence of interest in detecting signals with relatively low signal-to-noise ratio in the context of cognitive radios and spectrum sharing. A majority of existing algorithms rely on detection criteria which are carefully-crafted by domain experts to exploit specific features of the target signal. Motivated by the ability of deep neural networks to learn useful representations directly from raw data, in this work we use a deep temporal convolutional network for detection of signals, under multi-path fading and noise, directly from raw complex baseband samples with no other pre-processing or feature engineering.Our results indicate that the proposed deep learning approach outperforms a popular eigenvalue-based method without requiring any expert feature engineering. We further show that the performance is robust to new signal types not used during the training of the neural network.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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