Intelligent Spectrum Sensing of Consumer IoT Based on GAN-GRU-YOLO
Bibliographic record
Abstract
In the swift evolution of 5G cellular communication technology and Internet of Things (IoT), the consumer electronics market is booming. Consumer IoT has become an emerging industry. However, the development of the consumer IoT is subject to limited spectrum resources. Hence, this study suggests a smart spectrum sensing approach for consumer IoT based on GAN-GRU-YOLO. First, a Continuous Wavelet Transform (CWT) is used to capture frequency domain information from the received signals. A frequency domain feature matrix is constructed and then converted to a signal spectrogram to improve data diversity and enhance sensing. GAN is used to learn the signal spectrogram to generate more realistic synthetic data to achieve data enhancement and improve the classification performance of the overall model. Then, a two-branch GRU-YOLO network is employed to learn the signal characteristics in the time and frequency domains. The upper branch captures local feature information in the frequency domain and the YOLOv5 network captures high-level features. A combination of GRU and CNN in the lower branch extracts features from the data time series to ensure information continuity. Finally, the branch outputs are fused for further processing. The GAN-GRU-YOLO network has high generalization ability and efficiency. Compared with other methods, the proposed approach has a lower false alarm probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(P_{f})$ </tex-math></inline-formula> and a higher detection probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(P_{d})$ </tex-math></inline-formula>. At a signal-to-noise (SNR) ratio of -15 dB, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{d}$ </tex-math></inline-formula> is 11% to 65% higher and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{f}$ </tex-math></inline-formula> is 25% to 61% lower than the ResNet, MobileNet, Transformer and YOLOv6 algorithms.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".