Intelligent Spectrum Sensing Algorithm for Cognitive Internet of Vehicles Based on KPCA and Improved CNN
Why this work is in the frame
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Bibliographic record
Abstract
Abstract With the acceleration of economic globalization and integration, the global trade is becoming more frequent, which promotes the vigorous development of transportation industry. In recent years, the Internet of Vehicle (IoV) has developed rapidly in the transportation industry, and the number of IoV users has exploded. The requirements for IoV communication services are very high, resulting in the lack of spectrum resources. Rather than utilizing traditional spectrum resource allocation methods, cognitive radio technology makes full use of idle frequency bands, improving the IoV communication spectrum’s utilization rate. Spectrum sensing is the primary link to realize a cognitive radio. However, IoV mobile communication environment is characterized by complexity, dynamism, and substantial noise interference, thus imposing significant challenges to spectrum sensing. Thus, this paper proposes an intelligent spectrum sensing algorithm based on kernel principal component analysis (KPCA) and an improved convolutional neural network (CNN). Since the wireless signal cannot distinguish the signal and noise linearly, KPCA maps the sampled signal to a high-dimensional space, creates a covariance matrix, and obtains eigenvector data of the signal and noise through matrix decomposition. A spectrum sensing classifier based on improved CNN is proposed, and the dynamic threshold is obtained via offline training. Compared with the traditional algorithm, the designed deep CNN improves the model’s training speed, enables parameter sharing, and reduces the number of model parameters, effectively reducing the computational complexity. Additionally, due to the extracted signal feature’s small dimension, the algorithm reduces the number of pooling layers and avoids the effective features’ loss, thus increasing the detection probability. Finally, the proposed algorithm achieves a 10% higher sensing accuracy than support vector machine (SVM), Elman, and LeNet5 algorithms, signaling its robustness.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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