G-VAP: A Generative Variational Autoencoder Approach for Enhanced Cooperative Sensing
Bibliographic record
Abstract
In interweave cognitive radio (CR), users can transmit data only when licensed bands are vacant. Deep learning (DL) allows CRs to intelligently sense and identify channel availability. However, most supervised DL detectors require a substantial amount of labeled training data, which can be challenging to obtain in interweave CR. In this paper, we introduce G-VAP, an unsupervised deep generative approach for cooperative spectrum sensing (CSS). G-VAP utilizes an advanced variant of variational autoencoders (VAEs) called β-VAE to identify independent latent variables and encourage accurate and disentangled sensing data representation in a lower dimensional latent space. Furthermore, G-VAP utilizes the affinity propagation (AP) algorithm for unsupervised clustering to detect primary user activity. Unlike other unsupervised clustering methods, AP’s performance is not reliant on the initialization of cluster centroids. Furthermore, G-VAP leverages the cooperation among CRs to sustain a high detection performance. Our approach is fully data-driven, operates without any model-driven assumptions, and does not require prior knowledge of channel or signal characteristics for training. Numerical results indicate that G-VAP for CSS performs comparably to benchmark supervised DL-based CSS. Extensive simulations have been conducted in diverse network settings, propagation environments, and fading conditions, which have proved the effectiveness of G-VAP.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".