CNN-Based Spectrum Sensing Method for Low Probability of Detection Communication Systems
Bibliographic record
Abstract
In recent years, the development of Low Probability of Detection (LPD) communication systems has gained significant attention as a means to enhance communication security. Consequently, the need for effective signal interception technologies capable of detecting such signals has also increased. This paper proposes a novel spectrum sensing method based on Convolutional Neural Networks (CNNs) to determine the presence or absence of signals. The proposed method addresses the limitations of conventional energy detection techniques that rely on fixed thresholds, by learning diverse signal patterns to enable more accurate detection. Received signals are first sampled at a high rate and transformed into frequency-domain representations using the Fast Fourier Transform (FFT). These frequency spectra are then accumulated over time to form two-dimensional spectrograms, which are used as input to the CNN model. The proposed CNN classifier comprises four convolutional layers, along with batch normalization and pooling layers. Simulation results demonstrate that the proposed approach consistently outperforms traditional threshold-based energy detection methods, achieving approximately a 2 dB performance gain across all SNR conditions. Under –6 dB SNR, the method achieves an improvement of about 35% in detection accuracy.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".