DeepSense: An Unsupervised Deep Clustering Approach for Cooperative Spectrum Sensing
Bibliographic record
Abstract
Cognitive radio (CR) users can transmit data as vacant licensed bands become available. By using machine learning, CR users can intelligently sense channel activity and determine the availability of empty channels. Learning-based CR systems that use supervised learning for spectrum sensing require labeled training data. Furthermore, the majority of existing deep learning-based detectors are supervised, requiring a lot of labeled training data to achieve adequate performance. On the other hand, obtaining a large amount of labeled data in practical CR may be difficult. To address this gap, we propose DeepSense, which is an unsupervised cooperative sensing approach that uses representation learning by a sparse autoencoder (SAE) and unsupervised clustering by a Gaussian mixture model (GMM). DeepSense does not rely on cooperation among many SUs. Instead, it uses the learned representation to improve the detection performance, which significantly decreases the network's cooperation overhead. DeepSense does not require any prior knowledge, such as noise characteristics or channel state information, to operate. Furthermore, only a small amount of unlabeled data is needed for training. Extensive simulations have been conducted, which suggest that the proposed detector is able to learn hidden features in the sensing data that allows it to achieve an excellent detection performance. Moreover, our results show that DeepSense outperforms pure GMM, and attains comparable detection performance to benchmark deep supervised learning-based cooperative sensing.
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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 | 0.000 | 0.001 |
| 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 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".