Optimizing Spectrum Efficiency in Hybrid Cognitive Radios Through Unsupervised Learning
Bibliographic record
Abstract
The increasing demand for data transmissions in next-generation wireless networks necessitates effective spectrum utilization, a challenge addressed by cognitive radio (CR) through enhancing spectral efficiency. In hybrid underlay-interweave CR, secondary users (SUs) adapt their transmissions when primary users (PUs) are active to avoid causing interference and operate at full power during idle spectrum periods. The primary network's tolerance for interference is directly influenced by the currently active PUs. Consequently, the primary network's interference threshold exhibits a dynamic characteristic. By accurately determining the channel activity of the primary network, SUs can effectively optimize spectrum usage. This strategy enables the SUs to have higher transmit power when permitted, resulting in higher performance gains. Therefore, we propose an unsupervised learning framework for sensing in cooperative hybrid CR networks to precisely determine the channel state of the primary network. Our unsupervised approach utilizes principal component analysis (PCA) for feature preprocessing and dimensionality reduction and a Gaussian mixture model (GMM) for channel state identification. Furthermore, our approach requires no prior knowledge and learns on a small amount of unlabeled sensing data. Our findings suggest that our proposed CR network can accurately and efficiently determine primary network channel states based on a variety of performance metrics. Moreover, we demonstrate that the proposed unsupervised framework outper-forms popular supervised learning techniques. Furthermore, it is shown that the proposed learning approach offers reduced complexity and is robust to low signal-to-noise ratios.
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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 | 0.001 | 0.001 |
| 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".