CNN-Enabled Multiple Power-Levels Identification in Cognitive Radio Networks
Bibliographic record
Abstract
Spectrum sensing with transmit power identification can greatly facilitate the application of the hybrid spectrum access strategy in cognitive radio (CR) networks. Conventional model-driven methods suffer from severe performance degradation in low signal-to-noise ratio (SNR) regime. In this paper, we propose a multiple transmit power levels identification network (TPIN) which consists of three components. In the data preprocessing components, the covariance matrix (COV) of the received data is first employed as the observation data. Then, the residual network (ResNet) based feature extractor components is used to construct the test statistic by extracting high-dimensional features of the observation data. Furthermore, the likelihood ratio test (LRT) criterion is leveraged to design the cost function for obtaining the maximum posterior probability in the classifier components. Different from the assumption in conventional method, the prior probability of each transmit power levels is unknown to the TPIN, and the array of training set is randomly disturbed. In addition, in order to verify the ability of TPIN in data features extraction, a comparison reference experiment using a general test statistic (e.g., higher-order cumulative) as the observation data is introduced. Finally, simulation results demonstrate the identification performance of the COV-based (COV-TPIN) scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 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".